A real-time people tracking system based on trajectory estimation using single field of camera view

Hdioud, Boutaina; Ez-Zahout, Abderrahmane; Hadi, Youssef; Thami, Rachid Oulad Haj

doi:10.1109/iccat.2013.6522038

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Current systems for security cameras [98], [99], [100], [101] have proposed a framework for tracking the moving objects such as people and collecting the motion trajectories. The object trajectories are then analyzed in order to distinguish the normal activities from anomalous ones.…”

Section: Related Workmentioning

confidence: 99%

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Real-time data mining for multimedia streaming

Banihashemi¹

2021

Preprint

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Developing a Web Video Player connected to a security surveillance camera for collecting the video streams is the main objective of this study. The Developed Web Application tracks the target object through the sequences of video frames and generates the object trajectories. The video frames are analyzed, and the object trajectories are fed into a classifier or clustering method for training and movement detection purposes. In this thesis, several machine learning techniques are applied and implemented in Batch and in Real-Time mode including SVM, J48 Decision Tree, PART, Decision Table, Decision Stump, Multilayer Perceptron, and K-Means clustering by using two customized datasets. The object tracking, and movement detection are based on a simplified HSV color space model. The developed Web Application and proposed architecture are implemented on a local area network with in-house Server as well as a single computer and can detect the trajectories of the moving objects effectively.

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Section: Related Workmentioning

confidence: 99%

“…The object trajectories are then analyzed in order to distinguish the normal activities from anomalous ones. In [98], a Harris Detector is used to locate the points of interest. Then, the center of gravity of these interest points is calculated and represented as the trajectory of a moving person.…”

Section: Related Workmentioning

confidence: 99%

Real-time data mining for multimedia streaming

Banihashemi¹

2021

Preprint

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“…robotics, humanoid robotics, assistive devices for visually impaired). Harris corner detection algorithm is usually used for such characteristics detection because of its algorithmic simplicity and expected level of detection accuracy [1]. For feature tracking, several candidates exist, however only few of them respect the constraints of video real-time.…”

Section: Introductionmentioning

confidence: 99%

Zynq FPGA based and Optimized Design of Points of Interest Detection and Tracking in Moving Images for Mobility System

Amara¹,

Atri²,

Pissaloux³

et al. 2018

ijacsa

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In this paper, an FPGA based mobile feature detection and tracking solution is proposed for complex video processing systems. Presented algorithms include feature (corner) detection and robust memory allocation solution to track in real-time corners using the extended Kalman filter. Target implementation environment is a Xilinx Zynq SoC FPGA based. Using the HW/SW partitioning flexibility of Zynq, the ARM dual core processor performance and hardware accelerators generated by Xilinx SDSOC and Vivado HLS tools improve the system ability of processing video accurately with a high frame rate. Several original innovations allow to improve the processing time of the whole system (detection and tracking) by 50% as shown in experimental validation (tracking of visually impaired during their outdoor navigation).

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“…After detecting the objects in motion, we will limit the search areas of our moving object and we bounded them with the minimal rectangles, then a Harris detector [4] is applied for positioning the points of interest [5] on these objects of interest. Once these points of interest are identified in the sequence, we propose to compute the HOG descriptor on these points instead of applying it on the whole image, that is to say, these descriptors are calculated around each region of interest identified by the Harris detector.…”

Section: Proposed Approach For the Detection Of People On The Movementioning

confidence: 99%

“…The most common prototype for performing background subtraction is to find an explicit model of background. Foreground objects (objects in motion) are then detected by calculating the difference between the current frame and this background model [5].…”

Section: Proposed Approach For the Detection Of People On The Movementioning

confidence: 99%

People Detection in Complex Scenes By Using An Improved and Robust HOG Descriptor

Hdioud

Rachid

Mohammed³

2017

AIVP

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The detection of moving people in a complex scene filmed with a single camera is among the most difficult fields of research in vision by computer. In this work, we suggest improving the quality of detection methods based on the histogram of oriented gradients (HOG) descriptor. For that, we purpose to use a combination of type detector/descriptor to minimize the rate of the false detection produced by the descriptor HOG. The implementation of this combination as well as its evaluation on public bases show clearly that the technique which we propose produces many good results at the level of the detection of the people in movement compared with the descriptor HOG

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A real-time people tracking system based on trajectory estimation using single field of camera view

Cited by 5 publications

References 4 publications

Real-time data mining for multimedia streaming

Real-time data mining for multimedia streaming

Zynq FPGA based and Optimized Design of Points of Interest Detection and Tracking in Moving Images for Mobility System

People Detection in Complex Scenes By Using An Improved and Robust HOG Descriptor

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