Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification

Chen, Long; Ai, Haizhou; Zhuang, Zijie; Shang, Chong

doi:10.1109/icme.2018.8486597

Cited by 330 publications

(182 citation statements)

References 20 publications

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“…We only apply the bounding box regressor and classifier to obtain new b k t and s k t , respectively. The MOTChallenge public benchmark includes multiple methods [30,9,13] which classify the given detections with trained neural networks, hence, we consider our processing of the given detections also as public.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Tracking Without Bells and Whistles

Bergmann

Meinhardt²,

Leal-Taixé³

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

862

653

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The problem of tracking multiple objects in a video sequence poses several challenging tasks. For tracking-bydetection, these include object re-identification, motion prediction and dealing with occlusions. We present a tracker (without bells and whistles) that accomplishes tracking without specifically targeting any of these tasks, in particular, we perform no training or optimization on tracking data. To this end, we exploit the bounding box regression of an object detector to predict the position of an object in the next frame, thereby converting a detector into a Tracktor. We demonstrate the potential of Tracktor and provide a new state-of-the-art on three multi-object tracking benchmarks by extending it with a straightforward re-identification and camera motion compensation.We then perform an analysis on the performance and failure cases of several state-of-the-art tracking methods in comparison to our Tracktor. Surprisingly, none of the dedicated tracking methods are considerably better in dealing with complex tracking scenarios, namely, small and occluded objects or missing detections. However, our approach tackles most of the easy tracking scenarios. Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions. Overall, Tracktor yields superior tracking performance than any current tracking method and our analysis exposes remaining and unsolved tracking challenges to inspire future research directions.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Although not specifically provided, we followed the convention to also process raw DPM detections for MOT17. Note, several other public trackers already work on raw detections [30,9,13] and their own classification score and NMS procedure. Therefore, we consider the comparison with public trackers as fair.…”

Section: B2 Raw Dpm Detectionsmentioning

confidence: 99%

Tracking Without Bells and Whistles

Bergmann

Meinhardt²,

Leal-Taixé³

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

862

653

View full text Add to dashboard Cite

show abstract

“…For the sake of fairness, performance comparison was separately carried out according to onshore or onboard datasets, because the results of some trackers can be biased due to camera motion. We discuss the comparison with state-of-the-art tracking methods, such as the framework of Markov decision process (MDP) [47], the combination of Kalman filter and Hungarian assignment algorithm (SORT) [48], the kernel correlation filter method (KCF) [49], the combination of Kalman filter and Kuhn-Munkres (POI) [50], SORT with deep association metric (DeepSORT) [19], and candidate selection combined with re-identification (MOTDT) [51].…”

Section: Qualitative Resultsmentioning

confidence: 99%

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

et al. 2019

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It is crucial for unmanned surface vessels (USVs) to detect and track surrounding vessels in real time to avoid collisions at sea. However, the harsh maritime environment poses great challenges to multitarget tracking (MTT). In this paper, a novel tracking by detection framework that integrates the multimodel and multicue (M3C) pipeline is proposed, which aims at improving the detection and tracking performance. Regarding the multimodel, we predicted the maneuver probability of a target vessel via the gated recurrent unit (GRU) model with an attention mechanism, and fused their respective outputs as the output of a kinematic filter. We developed a hybrid affinity model based on multi cues, such as the motion, appearance, and attitude of the ego vessel in the data association stage. By using the proposed ship re-identification approach, the tracker had the capability of appearance matching via metric learning. Experimental evaluation of two public maritime datasets showed that our method achieved state-of-the-art performance, not only in identity switches (IDS) but also in frame rates.

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“…To deal with these challenging issues, many researchers have established a wide variety of approaches to overcome this tracking task. With the advancement of object detection, the tracking-by-detection framework has been quite successful to track multiple humans in video [7]- [11]. There are effectively two tracking modes in this framework: online and offline tracking.…”

Section: Introductionmentioning

confidence: 99%

“…Offline tracking approaches [8], [10], [12] employ both past and future detections to globally formulate an optimization problem, which is unsuitably applied in real world applications. Online tracking approaches [4], [5], [7], [9], [11], [13] achieve the tracking estimates only relying on detections from past and current time.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking

Angelini

Chambers

et al. 2019

IEEE Trans. Multimedia

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In this paper, we propose a multi-level cooperative fusion approach to address the online multiple human tracking problem in a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter framework. The proposed fusion approach consists essentially of three steps. Firstly, we integrate two human detectors with different characteristics (full-body and bodyparts), and investigate their complementary benefits for tracking multiple targets. For each detector domain, we then propose a novel Discriminative Correlation Matching (DCM) model, and fuse it with spatio-temporal information to address ambiguous identity association in the GM-PHD filter. Finally, we develop a robust fusion center with virtual and real zones to make a global decision based on preliminary candidate targets generated by each detector. This center also mitigates the sensitivity of missed detections in the Generalized Covariance Intersection (GCI) fusion process, thereby improving the fusion performance and tracking consistency. Experiments on the MOTChallenge Benchmark demonstrate the proposed method achieves improved performance over other state-of-the-art RFS based tracking methods.

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Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification

Cited by 330 publications

References 20 publications

Tracking Without Bells and Whistles

Tracking Without Bells and Whistles

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking

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