Constrained multi-target tracking for team sports activities
- Rikke Gade^{1}Email authorView ORCID ID profile and
- Thomas B. Moeslund^{1}
https://doi.org/10.1186/s41074-017-0038-z
© The Author(s) 2018
Received: 22 August 2016
Accepted: 21 December 2017
Published: 16 January 2018
Abstract
In sports analysis, player tracking is essential to the extraction of statistics such as speed, distance and direction of motion. Simultaneous tracking of multiple people is still a very challenging computer vision problem to which there is no satisfactory solution. This is especially true for sports activities, for which people often wear similar uniforms, move quickly and erratically, and have close interactions with each other. In this paper, we introduce a multi-target tracking algorithm suitable for team sports activities. We extend an existing algorithm by including an automatic estimation of the occupancy of the observed field and the duration of stable periods without people entering or leaving the field. This information is included as a constraint to the existing offline tracking algorithm in order to construct more reliable trajectories. On data from two challenging sports scenarios—an indoor soccer game captured with thermal cameras and an outdoor soccer training session captured with RGB camera—we show that the tracking performance is improved on all sequences. Compared to the original offline tracking algorithm, we obtain improvements of 3–7% in accuracy. Furthermore, the method outperforms two state-of-the-art trackers.
Keywords
1 Introduction
Sports analysis is an important research field, supporting a growing interest in data for statistical analysis of performance [1]. From recreational athletes wishing to track their own activities to professional teams, risking millions of dollars by losing a game, the interest in reliable performance measures is huge. Creating spatio-temporal trajectories of players is one of the essential steps in extracting statistics such as speed, distance and direction of motion. Manual annotation of video data used to be the only option, but it was very time-consuming and expensive. Thanks to research in computer vision, video analysis is increasingly automated, but even after several years of research on tracking algorithms, consistent tracking of multiple people is still very challenging [2]. Human motion can be erratic, and interactions between people substantially complicates the task.
For tracking purposes, the optimal camera view is a perpendicular top view. This is often not possible to obtain, e.g. at outdoor sports fields or in temporary indoor installations, so occlusions between people are a major challenge. Moreover, video captured from a long distance, with people wearing similar team uniforms, result in nearly identical appearances. This lack of distinct appearance information makes re-identification after full occlusions impossible. Thus, we must rely on motion information, even though some activities, especially in sports, often include fast and erratic motion. To overcome some of these challenges, we suggest utilising the fact that most team sports activities take place within a certain area, often with a constant number of people present over longer time periods.
The main contribution of this paper is a method for improving tracking precision of sports activities and similar activities with multiple people within a given area by integrating an automatic and robust counting algorithm. The estimated numbers act as constraints—guiding the tracking algorithm in these very challenging situations. We test the method on two challenging sports datasets with people of similar appearance, as well as a more general tracking scenario with pedestrians in a courtyard environment.
The remaining part of this paper consists of the following sections: in Section 2, we discuss related work and then provide an overview of our proposed method in Section 3. Section 4 describes the counting algorithm, and Section 5 describes the tracking algorithm. Section 6 then combines those two methods in a constrained tracking approach. In the second-to-last section of the paper (7), the system is evaluated through tests and comparisons, and Section 8 concludes the paper.
2 Related work
Multi-target tracking is a popular area of research with fast progression and a large number of papers published each year [2]. Recent algorithms in this area can generally be divided into two main groups: online and offline approaches. Online methods are recursive, relying only on past observations, while offline approaches process a batch of frames in each iteration. Online methods include the classic Bayesian filters, such as Kalman filters [3] and Particle filters [4]. These are often applied in real-time applications, where processing time is crucial, and only past observations are available. However, in other applications, such as analysis of motion and behaviour, a time delay can be accepted in order to reach a higher precision. Batch processing approaches exploit more information and the possibility of running several iterations back and forth in time might help avoiding tracker drift.
For multi-target tracking in RGB images, offline approaches have become increasingly popular, due to their superior accuracy. Compared to online (recursive) approaches, offline methods have great advances in that they optimise trajectories over batches of frames. These methods all operate on a set of detections as input and aim at reconstructing the trajectories by optimising an objective function. The main difference between these algorithms lies in the formulation of the objective function and the strategy for optimisation. Among others, the optimisation task has been formulated as integer linear programme [5, 6], network flow programme [7–10], quadratic Boolean programme [11], energy minimisation [12, 13], generalised clique graphs [14, 15] and maximum weight-independent set problem [16]. Other approaches include searching a hypergraph using a local-to-global strategy [17, 18] and using a hierarchical association of detection responses [19]. Some work also focuses on improving the appearance model for solving ambiguities, e.g. by implementing an online learning approach for discriminative appearance models [20, 21].
Despite the large amount of work conducted in this field, big challenges still remain in many applications due to noise and ambiguities. From a likely noisy set of detections, the algorithm must construct an unknown number of trajectories. This task causes ambiguities and thereby errors or inaccuracies.
Most work mentioned above designs algorithms for general pedestrian tracking. Benchmark datasets within this area often feature a continuous flow of people entering and leaving the scene. The focus of this work is the tracking of players in team sports, which has different properties in people’s behaviour. One of these properties is that people mostly stay within the tracked area, compared to the continuous flow through the scene seen in typical pedestrian scenarios. The specific activities observed have been taken into account in the significant amount of research in multi-target tracking specifically for team sports videos [22, 23]. Recent methods developed for team sports suggest including context information like Game Context Features [24] and contextual trajectory information [25], improving tracking by modelling latent behaviour from team-level context dynamics [26] or by improving the detection step [27, 28].
In this work, we combine the generally well-performing offline tracking strategy with the knowledge of a constant number of players on the field over longer time periods. Specifically, we take advantage of automatic counting, which can help constrain the tracking problem by estimating the number of people present in the scene.
3 Overview
4 Counting people
In most applications, the recorded scene consists of an area where people move around freely and some possible entrance/exit areas. These entrance/exit areas might be only at the edge of the image or there might be doors in the scene. Assuming that people are not continuously moving in and out of the scene, the number of people observed in the scene will stay constant during several time periods. This is especially true in sports videos when capturing a well-defined court area with a constant number of players.
An estimation of this occupancy pattern can be calculated using the approach presented in [29], which will be described briefly in the remaining part of this section.
First, we must try to detect all people in each frame. As the cameras are static, background subtraction is applied for segmentations purposes, followed by automatic thresholding. The resulting binary objects are then examined and optionally split vertically or horizontal if they are likely to represent more than one person. This procedure is described in detail in [30].
In order to split video sequences into stable and unstable periods, we must detect when people are close to the border of the scene and therefore likely to leave or enter the tracking area. The border and tracking areas must be predefined manually for each scene. Periods with people detected within the border area should be flagged as unstable and observed for people leaving or entering the scene. All other periods are marked as stable periods and should contain the same number of people until the next period of border activity. Estimations of the number of people leaving and entering the scene during unstable periods are found by applying local tracking on people within the predefined area close to the border.
Estimating the number of people is done by frame-based detection succeeded by an graph optimisation algorithm, based on Dijkstra’s algorithm [31]. The graph optimisation interprets the stable periods as nodes and transitions (people leaving or entering the scene) as edges. All nodes and edges have a weight factor based on the detection and tracking results.
Figure 3 illustrates the graph approach. For each stable period, the number of people is represented by circles where a darker colour indicates a higher weight. The lines between two stable periods represent the transitions, also coloured darker for a higher weight. The path through this graph is optimised to the highest total weight.
For each video sequence, this counting algorithm collects timestamps, numbers and probability weights, which are then transferred to the tracking algorithm.
5 Tracking by energy minimization
E_{det} aims to keep the solution close to the detections. E_{app} utilises the appearance of different objects to disambiguate data association. E_{dyn} is the dynamic model, using a constant velocity model. E_{exc} is a mutual exclusion term, introducing the physical constraint that two objects cannot be present in the same space at the same time. The target persistence term E_{per} penalises trajectories with start or end points far from the image border. The last term E_{reg} is a regularisation term that favours fewer targets and longer trajectories. For an exact definition of each term, we refer to [13]. E_{app} will be discarded in this work, as no appearance information is extracted.
where F is the temporal length of trajectory i in frames and N is the total number of trajectories. Thus, the first part of the equation infers that the energy directly increases with the number of trajectories. The second part is the sum of the inverse length of all trajectories; hence, in the minimisation process, it favours long trajectories.
This tracking algorithm takes a detection file as input; thus, it can be applied on both RGB and thermal video, utilising our detection method described in Section 4.
6 Constraining the tracking algorithm
We aim to constrain the tracking algorithm to construct approximately n trajectories, where n is the number with the highest probability, estimated by the counting algorithm described in Section 4. Two relevant parameters can intuitively be formulated: the number of targets tracked per frame and the total number of trajectories in each stable period. Ideally, since we are only concerned about stable periods, the total number of trajectories within a period should correspond to the number of targets tracked in each frame. However, if the trajectory of one person is fragmented into shorter tracks, the total number of trajectories will increase while the correct number of targets can still be tracked in every frame. Likewise, if the target is lost during the sequence, the total number of trajectories might be correct, while some frames have fewer targets. Therefore, both measures might be valid parameters to include in the optimisation.
where P(s,n) is a discrete probability function constructed from the results of the counting algorithm, which returns the probability of n number of targets in stable period s. The number of targets is given either per frame i in n(i) or per stable period s in N(s). F is the total number of frames, and S is the total number of stable periods.
- 1.
Minimise number of targets (orig.)
- 2.
Maximise length of tracks (orig.)
- 3.
Constrain number of targets per frame (A)
- 4.
Constrain number of tracks per stable period (B)
A negative sign is applied to the two new terms in order to make the optimal solution a minimum value. A weight (w_{1}, w_{2}) is added to each term, adjusting the influence from each term. These weights will be fitted during an optimisation process, described in Section 7.
7 Evaluation
7.1 Datasets
To prove the robustness of our proposed method, we test on two different sports datasets. One is captured with a thermal camera at an indoor sports arena, while the other is captured with an RGB fisheye camera at an outdoor soccer field. Thermal imaging is used for privacy reasons in the public indoor sports arena. Both datasets demonstrate the typical challenge with the similar appearance of sports players.
The second dataset is 30 s of video captured at an outdoor soccer field. Twenty-five people are present in most frames, performing different exercises related to soccer. The images are captured with an RGB fisheye camera (Hikvision DS-2CD6362F-I(S)(V)) with a resolution of 2048 × 2048 pixels with 15 fps. The images are cropped to the region of interest at a final resolution of 876 × 827 pixels. The dimensions of the observed field area are 52 × 68 m, and the camera is mounted approx. 10 m above the ground. Figure 1 shows a frame from this dataset.
We find that there is a lack of publicly available team sports datasets suitable for multi-target tracking. We will contribute to building a wide purpose dataset by publishing the thermal soccer sequences along with annotations for tracking on our website^{2}.
7.2 Weight parameters
The parameters of the original energy function, Eq. 3, are adjusted to the sports scenario, where we discard the appearance term (α) and weigh the dynamic model (β) with 0.5, due to the erratic motion often observed in sports. The remaining terms are weighted equally: α = 0,β = 0.5,γ = 1,δ = 1,ε = 1.
The weight parameters w_{1} and w_{2} introduced in Section 5 are fitted experimentally in order to adjust the influence of each term. We use the 30 s training sequence, described in Section 7.1. Combinations of the following parameter values are tested for w_{1} and w_{2}: {0, 0.1, 1, 10, 20, 100, 250, 500, 750, 1000}.
The results seem to be slightly more sensitive to w_{1}, where the accuracy is highest at w_{1} = 500, while the accuracy varies less than 0.1% with w_{2} values from 250 to 1000. We fix w_{2} = 500. The high values are explained by non-normalised terms of the energy function.
7.3 Counting
Figure 6 shows that sports sequences 6a–d are dominated especially by stable periods, which is one of the main reasons we propose this method for team sports applications.
7.4 Comparison
We compare the results of our method to the original implementation of the tracking algorithm presented in [13]. Furthermore, we compare it to two different tracking algorithms suitable for multi-target tracking with objects of similar appearance. The first is an online tracking algorithm based on the Kalman filter, as described and implemented in [32]. The second algorithm, called SMOT, is a recent algorithm showing state-of-the-art results and chosen because it is specifically aimed at tracking objects of similar appearance [33]. We apply the publicly available implementation of this tracker using the IHTLS similarity method. Three parameters should be fitted in order to adjust to the specific tracking scenario. We use our 30 s training sequence for experimentally fitting these parameters, given the following parameter values: min_s = 0.02,hor = 5 and eta_max = 1.
7.5 Results
where FN_{ t }, FP_{ t } and IDS_{ t } are the number of false negatives, false positives and ID switches, respectively, for time t, while g_{ t } is the true number of objects at time t.
Results—indoor thermal sequence 1
TP (%) | FP (%) | FN (%) | ID switch | MOTA (%) | |
---|---|---|---|---|---|
Kalman | 80.18 | 0.83 | 10.83 | 539 | 79.35 |
SMOT | 92.35 | 8.88 | 1.30 | 381 | 83.47 |
Original CEM | 91.43 | 2.22 | 5.70 | 172 | 89.22 |
Ours | 93.95 | 1.38 | 2.80 | 195 | 92.57 |
Results—indoor thermal sequence 2
TP (%) | FP (%) | FN (%) | ID switch | MOTA (%) | |
---|---|---|---|---|---|
Kalman | 73.46 | 0.37 | 14.27 | 728 | 73.09 |
SMOT | 95.03 | 17.74 | 0.47 | 267 | 77.29 |
Original CEM | 89.31 | 0.62 | 8.40 | 136 | 88.68 |
Ours | 92.82 | 0.30 | 5.03 | 128 | 92.51 |
Results—indoor thermal sequence 3
TP (%) | FP (%) | FN (%) | ID switch | MOTA (%) | |
---|---|---|---|---|---|
Kalman | 66.42 | 0.05 | 20.41 | 790 | 66.37 |
SMOT | 94.66 | 18.29 | 0.53 | 288 | 76.38 |
Original CEM | 86.15 | 0.02 | 10.24 | 217 | 86.13 |
Ours | 93.78 | 0.07 | 2.32 | 234 | 93.71 |
Results—outdoor RGB sequence
TP (%) | FP (%) | FN (%) | ID switch | MOTA (%) | |
---|---|---|---|---|---|
Kalman | 29.32 | 0 | 33.98 | 4098 | 29.31 |
SMOT | 60.93 | 0.04 | 15.98 | 2578 | 60.90 |
Original CEM | 72.83 | 0.01 | 19.42 | 865 | 72.83 |
Ours | 77.63 | 0.01 | 15.40 | 779 | 77.62 |
Results—courtyard thermal sequence
TP (%) | FP (%) | FN (%) | ID switch | MOTA (%) | |
---|---|---|---|---|---|
Kalman | 87.04 | 0.93 | 4.92 | 432 | 86.11 |
SMOT | 92.45 | 12.24 | 2.34 | 280 | 80.21 |
Original CEM | 88.13 | 1.38 | 3.72 | 438 | 86.76 |
Ours | 92.99 | 1.38 | 3.83 | 171 | 91.61 |
This subsequence is a typical example of how an occlusion between two players is handled. As shown in Fig. 7, the original tracker loses one of the targets (light blue in the top right corner) between frame 10 and 25. From frame 28, a new ID is assigned to that person. The proposed constrained tracker tracks both targets throughout the subsequence. However, the IDs switch between these two targets once (yellow and light blue).
7.6 GT numbers
Comparison between the MOTA results with automatic counting results and ground truth counting results as input
Indoor thermal 1 (%) | Indoor thermal 2 (%) | Indoor thermal 3 (%) | Outdoor RGB (%) | Courtyard thermal (%) | |
---|---|---|---|---|---|
Ours - aut. counting | 92.57 | 92.51 | 93.71 | 77.62 | 91.61 |
Ours - GT counting | 92.73 | 94.42 | 91.91 | 76.53 | 95.13 |
The results show that using a ground truth number as input to the tracking algorithm improves MOTA 0.16–3.52% on three sequences, while it gives a lower MOTA with 1.09–1.80% on the remaining two sequences. This indicates that errors in the counting algorithm do not have a large effect on the tracking result, as it is only implemented to guide the tracker. All results in Table 6 are better than the results produced by the original CEM tracker.
8 Conclusion
This work focuses on a robust tracking algorithm for team sports activities. We have shown how to combine an automatic counting algorithm with an offline tracking algorithm in order to constrain the number of tracks and improve reliability. The method is tested on four sports sequences from both indoor and outdoor scenes with 8 and 25 people, respectively, playing soccer and performing soccer-related exercises. Furthermore, we test a sequence of thermal video with pedestrians in a courtyard to prove the applicability for other scenarios. All results show superior performance compared to three state-of-the-art trackers.
We plan to test the proposed method on several other types of team sports and refine the algorithms accordingly. For future work in this area, we will consider integrating an automatically recognised sports type, as prior context knowledge on the specific sports type may inform the tracker in ambiguous situations.
Declarations
Authors’ contributions
RG has designed the method, performed the experiments, and prepared this manuscript. TBM has been supervising the work and revising the paper. Both authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
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