Multi-object tracking has been studied for decades. However, when it comes to tracking pedestrians in extremely crowded scenes, we are limited to only few works. This is an important problem which gives rise to several challenges. Pre-trained object detectors fail to localize targets in crowded sequences. This consequently limits the use of data-association based multi-target tracking methods which rely on the outcome of an object detector. Additionally, the small apparent target size makes it challenging to extract features to discriminate targets from their surroundings. Finally, the large number of targets greatly increases computational complexity which in turn makes it hard to extend existing multi-target tracking approaches to high-density crowd scenarios. In this paper, we propose a tracker that addresses the aforementioned problems and is capable of tracking hundreds of people efficiently. We formulate online crowd tracking as Binary Quadratic Programing. Our formulation employs target's individual information in the form of appearance and motion as well as contextual cues in the form of neighborhood motion, spatial proximity and grouping, and solves detection and data association simultaneously. In order to solve the proposed quadratic optimization efficiently, where state-of art commercial quadratic programing solvers fail to find the solution in a reasonable amount of time, we propose to use the most recent version of the Modified Frank Wolfe algorithm, which takes advantage of SWAP-steps to speed up the optimization. We show that the proposed formulation can track hundreds of targets efficiently and improves state-of-art results by significant margins on eleven challenging high density crowd sequences.
Multiple target tracking is one of the fundamental problems in computer vision. Most prior works have focused on low and medium density crowd sequences @cite_47 @cite_31 @cite_43 @cite_22 @cite_32 @cite_5 @cite_50 , where the goal is to design a better data association technique. Authors in @cite_22 formulate data association as maximum weight independent set. Many successful data association based trackers utilize network flow to formulate tracking @cite_33 @cite_19 @cite_52 . The solution to network flow can be found efficiently using linear programing @cite_52 or a dynamic programing @cite_19 . Authors in @cite_11 @cite_50 @cite_16 formulate data association as maximum clique problem. All of these methods assume that, the detections in each frame are already given. This requires having a good pre-trained object detector @cite_4 @cite_1 that works reasonably well.
@cite_31 @cite_4 @cite_22 @cite_33 @cite_1 @cite_32 @cite_52 @cite_43 @cite_19 @cite_50 @cite_5 @cite_47 @cite_16 @cite_11
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Multi-target tracking is an interesting but challenging task in computer vision field. Most previous data association based methods merely consider the relationships (e.g. appearance and motion pattern similarities) between detections in local limited temporal domain, leading to their difficulties in handling long-term occlusion and distinguishing the spatially close targets with similar appearance in crowded scenes. In this paper, a novel data association approach based on undirected hierarchical relation hypergraph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed undirected affinity graph. The relationships between different detections across the spatiotemporal domain are considered in a high-order way, which makes the tracker robust to the spatially close targets with similar appearance. Meanwhile, the hierarchical design of the optimization process fuels our tracker to long-term occlusion with more robustness. Extensive experiments on various challenging datasets (i.e. PETS2009 dataset, ParkingLot), including both low and high density sequences, demonstrate that the proposed method performs favorably against the state-of-the-art methods. 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. Multitarget tracking (MTT) hinges upon the solution of a data association problem in which observations across scans are partitioned into tracks and false alarms so that accurate estimates of true targets can be recovered. In this chapter, we describe a methodology for solving this data association problem as a maximum weight independent set problem (MWISP). This MWISP approach has been used successfully for almost a decade in fielded sensor systems using a multiple hypothesis tracking (MHT) framework, but has received virtually no attention in the tracking literature, nor has it been recognized as an application in the clique independent set literature. The primary aim of this chapter is to simultaneously fill these two voids. Second, we show that the MWISP formulation is equivalent to the multidimensional assignment (MAP) formulation, one of the most widely documented approaches for solving the data association problem in MTT. Finally, we offer a qualitative comparison between the MWISP and MAP formulations, while highlighting other important practical issues in data association algorithms that are commonly overlooked by the optimization community. The problem of associating data with targets in a cluttered multi-target environment is discussed and applied to passive sonar tracking. The probabilistic data association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, a new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter. The algorithm is applied to a passive sonar tracking problem with multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e., low) probabilities of detection at each sample time. A simulation result is presented for two heavily interfering targets illustrating the dramatic tracking improvements obtained by estimating the targets' states using joint association probabilities.