GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking

Dehghan, Afshin; Assari, Shayan Modiri; Shah, Mubarak

doi:10.1109/cvpr.2015.7299036

Cited by 284 publications

(155 citation statements)

References 17 publications

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“…The solution to network flow can be found efficiently using linear programing [10] or a dynamic programing [9]. Authors in [6], [23], [24] formulate data association as maximum clique problem. All of these methods assume that, the detections in each frame are already given.…”

Section: Related Workmentioning

confidence: 99%

Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes

Dehghan

Shah

2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-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 constraints, 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 answer 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.

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Section: Related Workmentioning

confidence: 99%

Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes

Dehghan

Shah

2018

IEEE Trans. Pattern Anal. Mach. Intell.

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“…In some approaches, the multi-frame data association problem has been formulated in a more specific class of problems, like for example minimum cost flow problems [5][6][7]12], binary integer programming [14], maximum weighted clique [13] or independent set [15]. The main advantage of such approaches is that efficient optimization methods designed for these problems can be directly employed to find the data association solution.…”

Section: Multi-frame Data Associationmentioning

confidence: 99%

“…Among offline techniques, global approaches perform the data association over all the frames simultaneously or by batch [5][6][7][8][9][10][11][12][13][14][15], whereas sliding window (a.k.a. multi-scan, near-online, or online with delay) methods optimize only a few recent frames at the same time [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking

Fagot-Bouquet

Audigier

Dhome

et al. 2016

Computer Vision – ECCV 2016

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Abstract. Multiple Object Tracking still remains a difficult problem due to appearance variations and occlusions of the targets or detection failures. Using sophisticated appearance models or performing data association over multiple frames are two common approaches that lead to gain in performances. Inspired by the success of sparse representations in Single Object Tracking, we propose to formulate the multi-frame data association step as an energy minimization problem, designing an energy that efficiently exploits sparse representations of all detections. Furthermore, we propose to use a structured sparsity-inducing norm to compute representations more suited to the tracking context. We perform extensive experiments to demonstrate the effectiveness of the proposed formulation, and evaluate our approach on two public authoritative benchmarks in order to compare it with several state-of-the-art methods.

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“…Then the best trajectories are composed by the subgraph whose nodes have the minimal cost. The tracker has been improved by Dehghan et al (2015).…”

Section: Pedestrian Tracking In Computer Visionmentioning

confidence: 99%

Simultaneous Detection and Tracking of Pedestrian From Panoramic Laser Scanning Data

Xiao

Vallet²,

Schindler

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, Nearest-point and Max-distance. Then, all the points on moving objects are transferred into a space-time (x, y, t) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches.

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GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking

Cited by 284 publications

References 17 publications

Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes

Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes

Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking

Simultaneous Detection and Tracking of Pedestrian From Panoramic Laser Scanning Data

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