Key-Track: A Lightweight Scalable LSTM-based Pedestrian Tracker for Surveillance Systems

Kulkarni, Pratik; Mohan, Shrey; Rogers, Sam; Tabkhi, Hamed

doi:10.1007/978-3-030-27272-2_18

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…At the same time, existing approaches typically analyzes the data offline with the ability to move forward and backward in time to maximize their algorithm accuracy scores, making edge deployable operation of these approaches impractical. In contrast to existing work, this article proposes a shift to non-personal and data private pedestrian tracking, improving upon our previous work in re-identification [40] and LSTM tracking [41] for a holistic algorithm pipeline and fully edge capable design.…”

Section: Related Work a Pedestrian Detection Re-identificationmentioning

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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Section: Related Work a Pedestrian Detection Re-identificationmentioning

confidence: 99%

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

Neff

Mendieta

Mohan

et al. 2020

IEEE Internet Things J.

Self Cite

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“…LSTMs are efficient techniques for the sequential linkage of observation data. In computer vision, they are mostly utilized for the processing of dynamically changing data such as motion behavior [21] and tracking of objects [11]. Not only temporal data can be processed by LSTMs: In [22], apple diseases and pests are detected.…”

Section: Related Papersmentioning

confidence: 99%

Active multiview recognition with hidden Markov temporal support

Nagy

Rashad

Czúni

2020

SIViP

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Our paper deals with active multiview object recognition focusing on the directional support of sequential multiple shots. Since inertial sensors are easily available nowadays, we propose the use of them to estimate the orientation change of the camera and thus to estimate the probability of relative poses. With the help of relative orientation change, we can compute transition probabilities between possible poses and can use a hidden Markov model to evaluate state (pose) sequences and can thus increase the recognition rate. Furthermore, we can plan our next viewing position to minimize the risk of misclassification, resulting in higher overall recognition rates. Besides giving the theoretical details, we use two datasets to illustrate the performance of our model through several tests including occlusion, blur, Gaussian noise, and to compare to a solution with a long short-term memory network.

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A robust deep networks based multi-object multi-camera tracking system for city scale traffic

Zaman

Bajwa

Saleem

et al. 2023

Multimed Tools Appl

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Key-Track: A Lightweight Scalable LSTM-based Pedestrian Tracker for Surveillance Systems

Cited by 4 publications

References 21 publications

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

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

Active multiview recognition with hidden Markov temporal support

A robust deep networks based multi-object multi-camera tracking system for city scale traffic

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Key-Track: A Lightweight Scalable LSTM-based Pedestrian Tracker for Surveillance Systems

Cited by 4 publications

References 21 publications

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

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

Active multiview recognition with hidden Markov temporal support

A robust deep networks based multi-object multi-camera tracking system for city scale traffic

Contact Info

Product

Resources

About

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

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