The task of multiple people tracking in monocular videos is challenging because of the numerous difficulties involved: occlusions, varying environments, crowded scenes, camera parameters and motion. In the tracking-by-detection paradigm, most approaches adopt person re-identification techniques based on computing the pairwise similarity between detections. However, these techniques are less effective in handling long-term occlusions. By contrast, tracklet (a sequence of detections) re-identification can improve association accuracy since tracklets offer a richer set of visual appearance and spatio-temporal cues. In this paper, we propose a tracking framework that employs a hierarchical clustering mechanism for merging tracklets. To this end, tracklet re-identification is performed by utilizing a novel multi-stage deep network that can jointly reason about the visual appearance and spatio-temporal properties of a pair of tracklets, thereby providing a robust measure of affinity. Experimental results on the challenging MOT16 and MOT17 benchmarks show that our method significantly outperforms state-of-the-arts.
Data association can be performed either on individual detections @cite_45 @cite_1 , or a set of confident and short tracklets @cite_7 @cite_62 which are generated by first performing a low level data association to group detections. A well-known representation of the tracking-by-detection paradigm is to present each detection as a node in a graph, with edges representing the likelihood that connected detections belong to the same person. This data association problem can be solved using Conditional Random Field inference @cite_25 , network flow optimization @cite_40 @cite_56 , maximum multi-clique @cite_42 , greedy algorithms @cite_26 , or subgraph decomposition @cite_28 .
@cite_62 @cite_26 @cite_7 @cite_28 @cite_42 @cite_1 @cite_56 @cite_40 @cite_45 @cite_25
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We present a detection-based three-level hierarchical association approach to robustly track multiple objects in crowded environments from a single camera. At the low level, reliable tracklets (i.e. short tracks for further analysis) are generated by linking detection responses based on conservative affinity constraints. At the middle level, these tracklets are further associated to form longer tracklets based on more complex affinity measures. The association is formulated as a MAP problem and solved by the Hungarian algorithm. At the high level, entries, exits and scene occluders are estimated using the already computed tracklets, which are used to refine the final trajectories. This approach is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results show a great improvement in performance compared to previous methods. Data association is the backbone to many multiple object tracking (MOT) methods. In this paper we formulate data association as a Generalized Maximum Multi Clique problem (GMMCP). We show that this is the ideal case of modeling tracking in real world scenario where all the pairwise relationships between targets in a batch of frames are taken into account. Previous works assume simplified version of our tracker either in problem formulation or problem optimization. However, we propose a solution using GMMCP where no simplification is assumed in either steps. We show that the NP hard problem of GMMCP can be formulated through Binary-Integer Program where for small and medium size MOT problems the solution can be found efficiently. We further propose a speed-up method, employing Aggregated Dummy Nodes for modeling occlusion and miss-detection, which reduces the size of the input graph without using any heuristics. We show that, using the speedup method, our tracker lends itself to real-time implementation which is plausible in many applications. We evaluated our tracker on six challenging sequences of Town Center, TUD-Crossing, TUD-Stadtmitte, Parking-lot 1, Parking-lot 2 and Parking-lot pizza and show favorable improvement against state of art. We address the problem of multi-person tracking in a complex scene from a single camera. Although tracklet-association methods have shown impressive results in several challenging datasets, discriminability of the appearance model remains a limitation. Inspired by the work of person identity recognition, we obtain discriminative appearance-based affinity models by a novel framework to incorporate the merits of person identity recognition, which help multi-person tracking performance. During off-line learning, a small set of local image descriptors is selected to be used in on-line learned appearances-based affinity models effectively and efficiently. Given short but reliable track-lets generated by frame-to-frame association of detection responses, we identify them as query tracklets and gallery tracklets. For each gallery tracklet, a target-specific appearance model is learned from the on-line training samples collected by spatio-temporal constraints. Both gallery tracklets and query tracklets are fed into hierarchical association framework to obtain final tracking results. We evaluate our proposed system on several public datasets and show significant improvements in terms of tracking evaluation metrics. This paper addresses the problem of simultaneous tracking of multiple targets in a video. We first apply object detectors to every video frame. Pairs of detection responses from every two consecutive frames are then used to build a graph of tracklets. The graph helps transitively link the best matching tracklets that do not violate hard and soft contextual constraints between the resulting tracks. We prove that this data association problem can be formulated as finding the maximum-weight independent set (MWIS) of the graph. We present a new, polynomial-time MWIS algorithm, and prove that it converges to an optimum. Similarity and contextual constraints between object detections, used for data association, are learned online from object appearance and motion properties. Long-term occlusions are addressed by iteratively repeating MWIS to hierarchically merge smaller tracks into longer ones. Our results demonstrate advantages of simultaneously accounting for soft and hard contextual constraints in multitarget tracking. We outperform the state of the art on the benchmark datasets.