Tracking Multiple People Online and in Real Time

Ristani, Ergys; Tomasi, Carlo

doi:10.1007/978-3-319-16814-2_29

Cited by 43 publications

(56 citation statements)

References 32 publications

(58 reference statements)

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“…Formulations with polynomial time solutions consider evidence along paths of time-consecutive edges [8,16,31,37,38,39,55,75,78] and some build on bipartite matching [12,14,20,26,42,62,71]. Methods that use all pairwise terms, not only timeconsecutive ones, are significantly more accurate but NPhard [18,25,27,28,41,58,61,65,66,67]. Unary terms are sometimes added for completeness [28,65].…”

Section: Related Workmentioning

confidence: 99%

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Features for Multi-target Multi-camera Tracking and Re-identification

Ristani

Tomasi

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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270

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Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images of people similar to a person query image. We learn good features for both MTMCT and Re-ID with a convolutional neural network. Our contributions include an adaptive weighted triplet loss for training and a new technique for hard-identity mining. Our method outperforms the state of the art both on the DukeMTMC benchmarks for tracking, and on the Market-1501 and DukeMTMC-ReID benchmarks for Re-ID. We examine the correlation between good Re-ID and good MTMCT scores, and perform ablation studies to elucidate the contributions of the main components of our system. Code is available 1 .

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Section: Related Workmentioning

confidence: 99%

“…Identities can be optimized jointly [28] or iteratively [74]. We choose correlation clustering [4,57,58] to trade off computational cost for simplicity of formulation and accuracy. This formulation considers evidence from all pairwise terms and optimizes identities jointly.…”

Section: Related Workmentioning

confidence: 99%

Features for Multi-target Multi-camera Tracking and Re-identification

Ristani

Tomasi

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Self Cite

461

270

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show abstract

“…In particular, they describe a dataflow platform for vision queries that is built on top of the SCOPE dataflow engine, that offers general SQL syntax, and supports added user-defined operators such as extractors, processors, reducers, and combiners. [49] proposed a method for single camera multi-target tracking in terms of the Binary Integer Program, and can incur online, real-time results on hardware. However, the system does not scale to multi-camera systems.…”

Section: B Iot Systems For Edge Video Analyticsmentioning

confidence: 99%

REVAMP²T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking

Neff

Mendieta

Mohan

et al. 2020

IEEE Internet Things J.

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This article presents REVAMP 2 T, Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking, as an integrated end-to-end IoT system for privacybuilt-in decentralized situational awareness. REVAMP 2 T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e. video cameras). On the algorithm side, REVAMP 2 T proposes a unified integrated computer vision pipeline for detection, re-identification, and tracking across multiple cameras without the need for storing the streaming data. At the same time, it avoids facial recognition, and tracks and re-identifies pedestrians based on their key features at runtime. On the IoT system side, REVAMP 2 T provides infrastructure to maximize hardware utilization on the edge, orchestrates global communications, and provides system-wide re-identification, without the use of personally identifiable information, for a distributed IoT network. For the results and evaluation, this article also proposes a new metric, Accuracy • Efficiency (AE), for holistic evaluation of IoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP 2 T outperforms current state-of-the-art by as much as thirteen-fold AE improvement.

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“…Evaluation is performed, similar to other works, on the DukeMTMC [21,20] [7] and are used in this evaluation.…”

Section: Dukemtmcmentioning

confidence: 99%

Multi Target Tracking from Drones by Learning from Generalized Graph Differences

Ardö¹,

Nilsson

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Formulating the multi object tracking problem as a network flow optimization problem is a popular choice. In this paper an efficient way of learning the weights of such a network is presented. It separates the problem into one embedding of feasible solutions into a one dimensional feature space and one optimization problem. The embedding can be learned using standard SGD type optimization without relying on an additional optimizations within each step. Training data is produced by performing small perturbations of ground truth tracks and representing them using generalized graph differences, which is an efficient way introduced to represent the difference between two graphs. The proposed method is evaluated on DukeMTMCT with competitive results.

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Tracking Multiple People Online and in Real Time

Cited by 43 publications

References 32 publications

Features for Multi-target Multi-camera Tracking and Re-identification

Features for Multi-target Multi-camera Tracking and Re-identification

REVAMP²T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking

Multi Target Tracking from Drones by Learning from Generalized Graph Differences

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Tracking Multiple People Online and in Real Time

Cited by 43 publications

References 32 publications

Features for Multi-target Multi-camera Tracking and Re-identification

Features for Multi-target Multi-camera Tracking and Re-identification

REVAMP2T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking

Multi Target Tracking from Drones by Learning from Generalized Graph Differences

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Product

Resources

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REVAMP²T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking