- Research Paper
- Open Access
Sample-based integrated background subtraction and shadow detection
© The Author(s) 2017
- Received: 25 May 2017
- Accepted: 7 December 2017
- Published: 22 December 2017
This paper presents an integrated background subtraction and shadow detection algorithm to identify background, shadow, and foreground regions in a video sequence, a fundamental task in video analytics. The background is modeled at pixel level with a collection of previously observed background pixel values. An input pixel is classified as background if it finds the required number of matches with the samples in the model. The number of matches required with the samples in the model to classify an incoming pixel as background is continuously adapted at pixel level according to the stability of pixel observations over time, thereby making better use of samples in dynamic as well as stable regions of the background. Pixels which are not classified as background in the background subtraction step are compared with a pixel-level shadow model. The shadow model is similar to the background model in that it consists of actually observed shadowed pixel values. Sample-based shadow modeling is a novel approach that solves the highly difficult problem of accurately modeling all types of shadows. Shadow detection by matching with the samples in the model exploits the recurrence of similar shadow values at pixel level. Evaluation tests on various public datasets demonstrate near state-of-the-art background subtraction and state-of-the-art shadow detection performance. Even though the proposed method contains shadow detection processing, the implementation cost is small compared with existing methods.
- Background subtraction
- Change detection
- Shadow detection
The use of change detection algorithms to automatically segment a video sequence from a stationary camera into background and foreground regions is a crucial first step in several computer vision applications. Results from this low-level task are often used for higher level tasks such as tracking, counting, recognition, and classification. The foreground regions correspond to the objects of our interest, for example, vehicles or people. In a real-world scenario, there are several problems which make change detection a more challenging problem than a simple moving/static classification. One is dynamic background which are regions in the background exhibiting nuisance motion like tree branches or flags swaying in the wind, ripples on water, and fountains. A good change detection algorithm should classify such regions of irrelevant motion as background to exclude them from further analysis.
Another concern for change detection algorithms is intermittent object motion. A foreground object which temporarily becomes static is to be retained in the foreground (e.g., a car stopped at traffic light or a person standing still). A related challenge is that of moved background objects.
An additional major challenge for change detection algorithms is presented by cast shadows which accompany foreground objects. Unless explicit handling is done, background subtraction algorithms tend to classify cast shadows as part of the foreground, detrimental to the subsequent stages of analysis. For example, in an intelligent traffic surveillance system, shadows can distort the shape of detected vehicles or cause multiple vehicles to be merged into one, which often leads to failure in subsequent content analysis steps.
The most common method for foreground segmentation is background subtraction. Modern background subtraction algorithms operate by first building a model for the background. An incoming frame is compared with the background model, and every pixel in the frame is classified as either background or foreground based on the similarity with the model. Pixels in the current frame that have significant disparities with the model are labeled as foreground. An incoming frame is also used to update the background model. Updating the background model is necessary since the model should adapt to various changes in the background like gradual or sudden illumination changes (changing time of the day, toggling of light switch, etc.), weather changes (rain, fog, etc.), or structural changes in the background. Most algorithms employ independent pixel-wise models for the background to aid fast hardware implementations. Post-processing operations like morphological or median filtering are often used to ensure some spatial consistency to the segmentation results.
2.1 Background subtraction
Many popular background subtraction algorithms operate by modeling the background with a probability density function (pdf) at each pixel. Wren et al.  used a Gaussian to model every pixel. The mean and variance of each pdf were estimated from incoming frames. An incident pixel value was classified as background or foreground depending on how well the pixel value fits with the estimated pdf. However, a single Gaussian model cannot handle multimodal backgrounds, such as waving trees. Stauffer and Grimson  addressed this issue by modeling each pixel with a mixture of K Gaussians. The mixture of Gaussians (MoG) model can be described by the mean, variance, and weight parameters for each of the K Gaussians. Each incoming pixel was matched to one of the K Gaussians if it was within 2.5 standard deviations from its mean. The parameters for that Gaussian were then updated with the new observation. Repeated similar observations drive the weight of the matched component up while simultaneously reducing its variance. If no match was found, a new Gaussian with mean equal to the current pixel value and some wide variance was introduced into the mixture. Background modeling using MoG has been widely used and improved upon by many others [10–12]. Zivkovic  presented a method to choose the right number of components for each pixel in an online fashion. Haines  presented a method based on Dirichlet process Gaussian mixture models.
An alternative to parametric methods like MoG is the non-parametric approach proposed by  and . Instead of modeling the background with a mixture of Gaussians, the pdf is estimated from the history of observations at individual locations without any prior assumptions about the form of the pdf. The non-parametric approach is attractive as it can handle unimodal and multimodal regions of the background. One drawback with non-parametric methods is that they incur a high memory cost to adequately model infrequent background modes. The conflicting requirements of modeling infrequent background events and low memory requirements was well addressed by the random sample replacement technique introduced in ViBe . The background was modeled at pixel level in a non-parametric manner with a collection of N previously observed pixel values, called samples. The samples do not represent the immediate history but is a random sampling from old and recent frames, in an attempt to capture sporadic as well as frequent background modes. The sample-based modeling used in ViBe was inspired by SACON , but they employed a FIFO filling where the N most recent samples were used to model each pixel. The Pixel-Based Adaptive Segmenter (PBAS)  and SuBSENSE  are other methods which used samples to model the background, but they also included gradient information and Local Binary Similarity Pattern (LBSP) features, respectively, in the model.
Sample-based methods have been compared favorably with traditional methods like Gaussian mixture modeling (GMM), and hence, we adopt such an approach. As in previous sample-based methods, we model the background at each pixel location with a set of sample pixel values, previously observed at the same location and judged to be belonging to the background. An incoming pixel value is compared with the samples in the model and is classified as background if its pixel value is closer than a distance threshold R to at least #min of the N samples in the model.
2.2 Shadow detection
In , the Gaussian mixture shadow model is tied together with the Gaussian mixture background model. Since the update speed for shadow model is faster than that for the background model, the shadow model can become the background model when a pixel shows frequent shadow activity. At the other end, when there is no shadow appearance for long periods, the Gaussian model for shadow can be removed from the mixture. Our method avoids both these problems as the background and shadow models are kept separate. Both models are always present, and they evolve independently.
In , the Gaussian mixture shadow model parameters can take a long time to converge, affecting detection performance during this training period. Our method avoids this problem as we use a non-parametric model. The shadow model is initialized directly from the background model by linearly attenuating the background values, and thus, there can be a complete absence of training period in the case of achromatic shadows. Chromatic shadows require just a couple of appearances before they are detected.
To accelerate pixel-level shadow GMM convergence of , Huang and Chen  used an additional global GMM shadow model which guides the weighting of samples in the local shadow model learning. Comparative studies on shadow detection algorithms can be found in [33, 34].
Previous sample-based methods used a global constant value for #min (the number of matches required with the samples in the model to classify an input pixel as background). This value had to be kept low to correctly classify background pixels in regions with multiple modalities (like waving trees or water). Since all pixels are modeled with the same number of samples, this resulted in an under-utilization of samples in stable regions of the background. The more stable the observations are, the higher #min should be as this will lead to enhanced detection. Therefore, in our method, the required number of matches #min is adapted for each pixel independently according to an estimate of background dynamics. This is our first main contribution. The pixel-level shadow model using shadow samples is our second main contribution. Note that other sample-based background modeling methods did not address the problem of shadow detection.
4.1 Background model initialization and labeling process
Here, each sample represents an RGB triplet. The background model is initialized blindly from the first N frames. A ghost suppression mechanism (discussed later) ensures that the model bootstraps in the event of foreground objects being present during the initialization period.
The decision process to classify an incoming pixel as background or foreground is a consensus: a pixel at location x in the input frame is classified as background if it matches with at least #min(x) of the N samples in its background model. An input pixel is declared to match a sample if its pixel value v(x) is closer than a distance threshold R in each of the three channels, that is if |v(x)−B i (x)|<R in each of the three channels. In contrast, ViBe  used Euclidean distance measure in the RGB space to determine whether a pixel value matched with samples. PBAS  considered each channel independently and combined the outputs using a logical OR operation. While the PBAS approach is faster than Euclidean distance computation, a downside with this approach is that a foreground pixel can be wrongly classified as background if it finds enough matches in any one of the channels. Our approach counts a match only if there is a match in all channels.
4.2 Adaptation of #min
ViBe , PBAS , and SuBSENSE  used the same global #min=2. The use of the same #min is questionable given that the three methods used different number of samples in the background model (N=20 in ViBe, N=35 in PBAS, and N=35 or 50 in SuBSENSE). If a global value is used for #min, it should be proportional to the number of samples. Second, as the authors of PBAS note, increasing the number of samples benefits only dynamic regions while performance saturates for stable background regions. This is because of the global #min. Stated differently, a low global value for #min prevents full utilization of samples in stable regions. This situation can be improved by using a variable #min for each pixel according to the behavior of the pixel, thereby making better use of the available N samples for all pixels: stable or dynamic. These observations led to our scheme of using a per-pixel required number of matches #min(x), where x denotes the pixel location. #min(x) is continuously adapted for each pixel separately based on an estimate of background dynamics (#min(x) is reduced in dynamic regions of the background and increased in static regions).
where the parameters α and β are optimized empirically, as discussed below. This form of the adaptation equation was hand-crafted as it allows for a single equation to handle the rise or decay of #min. No adaptation of #min(x) is done if x is a classified as foreground, because in this case, the number of matches is not a measure of background dynamics. Since dynamic behavior is usually exhibited over a region rather than by isolated pixels, a spatial smoothing of #min is then applied every frame using a 3×3 Gaussian filter. The spatial smoothing adapts #min for foreground pixels also, based on neighboring dynamics.
4.3 Background model update
Essentially, we follow a conservative update as this leads to enhanced foreground detection. This means that only a pixel value that has been classified as background is inserted in the model. Background pixels are updated with a probability of 1/16, as in . However, strict conservative update is relaxed by also updating small foreground blobs (below a size of 20 pixels) which are usually false detections due to dynamic background. We follow the random update introduced in  and adopted in  and , where a randomly chosen sample is replaced by the new value, as this extends the time window spanned by the model.
4.4 Ghost suppression mechanism
To differentiate between static foreground objects and wrongly detected “ghost” foreground blobs (due to incorrect initialization which includes foreground objects, segmentation errors, or moved background objects), boundary pixels of foreground blobs are identified and compared with neighboring background pixel values. Because ghost areas typically share similar color characteristics with neighboring regions whereas real foreground objects do not, the boundary pixel models are updated with the pixel value at the same location when there is a match with all neighboring background pixels and otherwise not updated. This results in the fast erosion of ghost areas while retaining real foreground objects in the foreground and prevents corruption of pixel models with irrelevant values, as caused by the regional diffusion of background information during update, employed in [17, 35]).
4.5 Post processing
A 3×3 median filtering is applied as the sole post-processing step. The resulting foreground mask is the input to the shadow detection step.
4.6 Background subtraction parameter settings
The parameters α and β were experimentally determined to be 19 and 2000 considering various videos. For some categories of Cdnet dataset such as dynamicBackground and cameraJitter, a slight improvement in F-measure was obtained with a smaller β of 1000 as #min adapted more quickly. However, the overall F-measure considering all categories was slightly lower. Values other than 19 for α resulted in lower F-measure for almost all videos.
Our shadow detection method follows a two-stage approach and can detect achromatic as well as chromatic shadows. Only pixels classified as foreground in the background subtraction step are considered as candidate shadow pixels because shadows labeled as background is not an issue in most applications. In the first stage, a weak shadow detector filters out impossible shadow values from the set of foreground pixels by imposing mild constraints on brightness, color distortion, and RGB values. Specifically, for brightness, the input pixel is constrained to have a brightness less than the background pixel. For chroma, we adopted the color distortion measure in [24, 36] as they avoided the instability of normalized RGB. The threshold for allowed color distortion is intentionally permissive to include chromatic shadows as well. These tests on brightness and chroma lend nicely to our sample-based model. The input pixel should have brightness less than at least #min samples in the background model and the same samples should be at a color distance less than a threshold with the input pixel. These two conditions alone tend to include dark objects as shadows since the color distortion between pixels is small near the origin of the RGB space. As noted in  and from our own analysis, R, G, and B values of a shadowed pixel are almost always above 20 (on a 0 to 255 scale), for all types of shadows. Therefore, this third condition is also incorporated in our initial shadow test (i.e., an input pixel should have R, G, and B values above 20 to be considered as a shadow pixel). Foreground pixels satisfying these constraints on brightness, chroma, and RGB values result in a mask M1 on the current frame. Because of the liberal constraints, this initial mask M1 may contain some pixels from foreground objects, but this is taken care of in the second step. At this stage, it is important only that all shadow pixels are included in M1.
The second stage exploits the recurrence of similar shadow values at pixel level. A pixel-level shadow model is populated with actual shadowed pixel values. Collecting shadowed samples in a separate model for each pixel is an effective way to represent the change in pixel value when it comes under shadow. This shadow-induced variation in pixel value is otherwise highly difficult to characterize because it depends on a huge number of factors such as nature and number of other light sources and reflective properties of the surface as well as that of other scene objects and texture properties of the surface. However, since these factors which determine the shadow pixel values are more or less stable, the values that a given pixel takes, when shadowed by different foreground objects, show a certain agreement even in complex illumination conditions . Based on this rationale that shadow pixel values repeat at pixel level, each pixel in the initial shadow mask M1 is compared with the pixel-level shadow model and those pixels which find at least #min matches with the samples in the shadow model are labeled as shadow in the final shadow mask M2.
5.1 Shadow model initialization and maintenance
A problem in modeling shadows when compared to modeling background is that shadow appearance is not as frequent as background appearance. For our method to work properly, it has to be ensured that the shadow model contains enough shadow samples for matching and detection. This is not an issue in busy highways and hallways where shadows appear frequently but has to be taken care of in cases where foreground activity is rare. We address this issue in multiple ways. First, the shadow model is initialized directly from the background model by linearly attenuating the RGB values of background samples. This readies the shadow model to detect achromatic shadows right from the first appearance. However, the scaling being linear, chromatic shadow pixels in mask M1 may not initially find matches with the shadow model, preventing final shadow labeling and update of shadow model. To avoid this deadlock situation, the shadow model is updated with the initial mask M1, without considering whether the pixel values are matched to the samples in the shadow model or not. This enables the shadow model to be filled with sufficient shadow samples for further matching. Of course, since the update mask M1 is based on test of brightness, color distortion, and RGB values only, it may cause some foreground values (which pass the weak detector test based on these properties) also to be inserted in the model. But this is not troublesome as foreground values are less likely to be repeated and matched than shadow values. The shadow model is updated with a higher probability of 1/4, again due to the less frequent appearance of shadows compared to background.
5.2 Shadow detection parameters
For the shadow model, we used number of samples N=20, required number of matches #min=2, and distance threshold R=20. The optimum scaling factor for initializing shadow model from background model will vary from sequence to sequence and also from pixel to pixel. A value of 0.6 was found to be appropriate in most cases. A hand-chosen value helps to reduce the training period in some cases. For instance, a larger attenuation factor can be used for red and green values than blue values to account for the observation in  that shadows falling on neutral structures like asphalt roads and concrete buildings tend to be more bluish. The overall performance is not very sensitive to the scaling factor since the model self corrects as it receives update. The color distance threshold was set to a large value of 30 to include all types of shadows in the update mask.
6.1 Background subtraction
Here, tp is the number of true positives (foreground), fp is the number of false positives, and fn is the number of false negatives.
SABS results: F-measures for various scenarios
Li et al.
Zivkovic and van der
Haines and Xiang
Van Droogenbroeck 
H.264 (40 kbps)
F-measures for 2012 CDnet
int. obj. motion
Other common performance metrics like recall, precision, specificity, false positive rate, false negative rate, and percentage of wrong classification as well as complete segmentation results for various methods, including ours, are publicly available under Results tab on the changedetection.net website. All metrics were obtained by uploading the segmentation results to the website and calculated using their software.
6.2 Shadow detection
Quantitative shadow detection results
Martel-Brisson and Zaccarin
Martel-Brisson and Zaccarin
Huang and Chen
Amato et al.
Huerta et al.
6.3 Computational cost
The speed advantage of sample-based methods compared to parametric methods like GMM has been reported in . Compared to ViBe , the proposed method offers a speed advantage which stems from the fact that L1 distances are used whereas  used L2 distances. Compared to (PBAS) , the proposed method uses fewer samples (35 vs 20) and hence lesser computational cost while still obtaining a better performance. Compared to SuBSENSE , which uses 50 samples per pixel, our method uses only 40 samples for combined background subtraction and shadow detection, resulting in a lower overall computational complexity. In addition, the proposed method has fewer parameters compared to PBAS and SuBSENSE which also helps in reducing the computational complexity.
Sample-based modeling has proven to outperform conventional methods of background modeling. The presented method addressed the issue of under-utilization of samples in stable regions of the background with continuous per-pixel adaptation of the number of matches required to classify a pixel as background. Experimental results have proven the robustness of our method in various scenarios. Shadow detection which was often done independently of background subtraction is integrated in our approach. Our novel approach of populating the shadow model with actually observed shadow pixel values solves the difficult problem of modeling different types of shadows. As shown by experimental results, our two-stage shadow detector can accurately model and reliably detect shadows, no matter what the illumination condition, geometry or texture of the background, and orientation or type of shadow are. Since both background and shadow models are at pixel level and employ only pixel values with few parameters, our combined background subtraction and shadow detection algorithm are well suited for fast hardware implementations.
The authors would like to thank all the people associated with the research lab of NIT Calicut for providing the facilities needed.
Both authors contributed equally for this work. AV designed and performed the experiments. SG supervised the work and contributed to the writing of the paper. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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