A Real-Time Active Pedestrian Tracking System Inspired by the Human Visual System

Wang, Yuxia; Zhao, Qingjie; Wang, Bo; Wang, Shixian; Guo, Wei; Feng, Zhiquan

doi:10.1007/s12559-015-9334-z

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…The motion information is rich in moving targets even in dynamic scenes, which presents new challenges of visual detection due to the mixed motion, which makes it difficult to discriminate the moving camera and the moving object(s). Inspired by the human visual system, a real-time pedestrian tracking system from a pan-tilt-zoom camera is proposed in [16]. In [17], a scale adaptive tracking method is proposed by combining translation and scale discriminative correlation filters.…”

Section: Introductionmentioning

confidence: 99%

Real‐time running detection system for UAV imagery based on optical flow and deep convolutional networks

Zhou

Wang

et al. 2020

IET Intelligent Trans Sys

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A fast-running human detection system for the unmanned aerial vehicle (UAV) based on optical flow and deep convolution networks is proposed in this study. In the system, running humans can be detected in real-time at the speed of 15 frames per second (fps) with an 81.1% detection accuracy. To fast locate the candidate targets, optical flow representing the motion information is calculated with every two successive frames. A series of prior-processing operations, including spatial average filtering, morphological expansion and outer contour extraction, are performed to extract the regions of interest. A classification model based on small-kernel convolution networks is proposed to achieve the accurate recognition of the running people in various backgrounds. In the model, small convolutional filters are adopted to accelerate the speed of the data representation. Moreover, a total of 60,000 samples are collected to enhance the robustness of the model to adapt to the complex outdoor UAV scenes. The proposed method is compared with other deep learning frameworks for object detection. Field experiments on UAV videos are performed to verify that the proposed system can effectively detect the running people targets in real-time.

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Section: Introductionmentioning

confidence: 99%

Real‐time running detection system for UAV imagery based on optical flow and deep convolutional networks

Zhou

Wang

et al. 2020

IET Intelligent Trans Sys

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“…However, background regions which have the similar color/intensity as the foreground may be detected as foreground by mistake. In Wang et al [31], a coarse-to-fine pedestrian detection method is proposed for visual surveillance, which can solve the problem in detecting small pedestrians. By using pan-tilt-zoom control, it also helps to achieve real-time tracking, though the performance depends on specified sensor settings.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Fusion of Thermal and Visible Imagery for Effective Detection and Tracking of Pedestrians in Videos

Yan¹,

Ren²,

Zhao

et al. 2017

Cogn Comput

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In this paper, we present an efficient framework to cognitively detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, the thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, cognitive fusion of these two modalities provides an effective solution to tackle this problem. First, a background model is extracted followed by a two-stage background subtraction for foreground detection in visible and thermal images. To deal with cases of occlusion or overlap, knowledge-based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. To evaluate the proposed method, a publicly available color-thermal benchmark dataset Object Tracking and Classification in and Beyond the Visible Spectrum is employed here. For our foreground detection evaluation, objective and subjective analysis against several state-of-the-art methods have been done on our manually segmented ground truth. For our object tracking evaluation, comprehensive qualitative experiments have also been done on all video sequences. Promising results have shown that the proposed fusion-based approach can successfully detect and track multiple human objects in most scenes regardless of any light change or occlusion problem.

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Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis

Gupta¹,

Borah

Sharma³

et al. 2021

Neural Comput & Applic

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A Real-Time Active Pedestrian Tracking System Inspired by the Human Visual System

Cited by 10 publications

References 35 publications

Real‐time running detection system for UAV imagery based on optical flow and deep convolutional networks

Real‐time running detection system for UAV imagery based on optical flow and deep convolutional networks

Cognitive Fusion of Thermal and Visible Imagery for Effective Detection and Tracking of Pedestrians in Videos

Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis

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