Adaptive pedestrian tracking via patch-based features and spatial–temporal similarity measurement

Shen, Xuewei; Sui, Xiubao; Pan, Kechen; Tao, Yuanrong

doi:10.1016/j.patcog.2015.11.017

Cited by 23 publications

(12 citation statements)

References 33 publications

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“…First is current passenger flow amount of key area 1 A , which can be real-timely obtained by surveillance sensors in key area. Second is average passenger flow amount of key area in statistic period 2 A and third is max passenger flow amount of key area in statistic period 3 A . The last two parameters can be obtained from historical passenger flow amount of key area in statistic period.…”

Section: Key Area Risk Value Calculation In In High-speed Railwaymentioning

confidence: 99%

“…The last two parameters can be obtained from historical passenger flow amount of key area in statistic period. According to the different relationship among 1 A , 2 A , 3 A , the key area risk value ( ) r x can be calculated as follows [9]: …”

Section: Key Area Risk Value Calculation In In High-speed Railwaymentioning

confidence: 99%

“…Several passenger image processing and intelligent detecting algorithms were developed for rapid and accurate detection of passenger flow status [1][2][3][4]. To improve detection accuracy in terms of feature extraction, a novel feature of DOI reference number: 10.18293/DMS2016-048 gradient self-similarity (GSS) was present, which was computed from HOG, and was applied to capturing the patterns of pairwise similarities of local gradient patches [1].…”

Section: Introductionmentioning

confidence: 99%

“…In order to accurately and promptly detect potential safety hazard, a modified background model based on Dempster-Shafer theory, and a passenger flow status recognition algorithm based on features of image connected domain were proposed to improve the accuracy and real-time performance of passenger flow detection [2]. For the challenging of abrupt illumination, occlusions, out of field of view, and cluttered backgrounds, a novel tracking framework was developed, which consists of two steps: image shadow removal and tracking by association [3]. A sparse representation based approach is proposed for pedestrian detection from thermal images.…”

Section: Introductionmentioning

confidence: 99%

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Temporal-Based Risk Forecasting Approach for Key Areas on Surveillance Sensor Networks of High-Speed Railway Transport Hub

Qin¹,

Wang²,

Xie³

2016

International Conferences on Distributed Multimedia Systems

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Abstract-Key areas risk forecasting plays an important roles in safety management in high-speed railway transport hub. In this paper, a temporal-based risk forecasting approach was considered for key areas on surveillance sensor networks. Computational experiments on a specific key area in high-speed railway transport hub were conducted to illustrate the proposed approach. The results showed the temporal-based forecasting approach is effective and efficient for key areas risk forecasting in high-speed railway transport hub.

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Section: Key Area Risk Value Calculation In In High-speed Railwaymentioning

confidence: 99%

Section: Key Area Risk Value Calculation In In High-speed Railwaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temporal-Based Risk Forecasting Approach for Key Areas on Surveillance Sensor Networks of High-Speed Railway Transport Hub

Qin¹,

Wang²,

Xie³

2016

International Conferences on Distributed Multimedia Systems

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“…Algorithm B is proposed with adaptation from work done by [27] with modifications being made before implementing it for this project. For Algorithm B, segmentation of potential shadow region is carried out by thresholding on L channel.…”

Section: B Algorithm Bmentioning

confidence: 99%

Development of Algorithm to Reduce Shadow on Digital Image

Ooi¹,

Ibrahim²

2016

IJIGSP

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Abstract-In this paper, two shadow reduction algorithms have been proposed and implemented using CIE Lab color space. The task of performing shadow reduction is done by executing shadow detection, shadow removal and lastly shadow edge correction in a sequential order. The first proposed algorithm is implemented based on pixel illumination and color information meanwhile the second algorithm is carried out via thresholding of one or more CIE Lab color space channels. The outputs from both proposed algorithms are compared in terms of shadow detection accuracy and required processing period. The proposed methods shown some promising results.

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An online multiple object tracker based on structure keeper net

Wang

Zou

et al. 2021

Appl Intell

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Adaptive pedestrian tracking via patch-based features and spatial–temporal similarity measurement

Cited by 23 publications

References 33 publications

Temporal-Based Risk Forecasting Approach for Key Areas on Surveillance Sensor Networks of High-Speed Railway Transport Hub

Temporal-Based Risk Forecasting Approach for Key Areas on Surveillance Sensor Networks of High-Speed Railway Transport Hub

Development of Algorithm to Reduce Shadow on Digital Image

An online multiple object tracker based on structure keeper net

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