Robust object tracking using local oriented energy features and its hardware/software implementation

Norouznezhad, Ehsan; Bigdeli, Abbas; Postula, Adam; Lovell, Brian C.

doi:10.1109/icarcv.2010.5707853

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…There many other features like orientation energy [10] , SIFT feature [11], optical flow and active contour [13]. In certain cases directional edge is considered as a features explains that animals are having excellent visual tracking ability due to visual perception of animals relies on directional edges [14].…”

Section: Implementation Of Real Time Moving Object Detection Using Bamentioning

confidence: 99%

Implementation of real time moving object detection using background subtraction in FPGA

Cherian

Singh

Manikandan

2014

2014 International Conference on Communication and Signal Processing

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Background subtraction is one of the techniques used in video surveillance system for detecting moving objects in a video. In this paper we propose a background subtraction method which gives output even when the camera is shaking.There are many challenges that we have to consider for developing a robust background subtraction method mainly illumination variation. Here the algorithm works in such a way that the input frames are compared and compensated with reference frame then separating the foreground object with respect to background. Background subtraction can be built very efficiently on an FPGA which can process 640x480 video sequence. Experimental result shows that it is possible to detect the moving object in a very fast manner. It is implemented in Digilent Atlys Spartan-6 FPGA development board can solve various problems like complex computation, data transmission, cost of hardware resources etc. The real time video is captured by using VmodCAM.

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Section: Implementation Of Real Time Moving Object Detection Using Bamentioning

confidence: 99%

Implementation of real time moving object detection using background subtraction in FPGA

Cherian

Singh

Manikandan

2014

2014 International Conference on Communication and Signal Processing

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“…At the same time, various software and very-large-scale integration (VLSI) hardware prototypes based on those motion energy models have been implemented [23]–[34]. In [23], a software approach utilized the estimated velocities to evaluate time to collision for obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

“…Their high system cost and power consumption are not suitable for embedded applications. Recently, a few digital VLSI prototypes based on field-programmable gate array (FPGA) platforms have been proposed [34], [35]. They provide lower cost, higher precision, and higher flexibility compared with software implementations and analog chips.…”

Section: Introductionmentioning

confidence: 99%

A Compact VLSI System for Bio-Inspired Visual Motion Estimation

Shi

Luo

2018

IEEE Trans. Circuits Syst. Video Technol.

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This paper proposes a bio-inspired visual motion estimation algorithm based on motion energy, along with its compact very-large-scale integration (VLSI) architecture using low-cost embedded systems. The algorithm mimics motion perception functions of retina, V1, and MT neurons in a primate visual system. It involves operations of ternary edge extraction, spatiotemporal filtering, motion energy extraction, and velocity integration. Moreover, we propose the concept of confidence map to indicate the reliability of estimation results on each probing location. Our algorithm involves only additions and multiplications during runtime, which is suitable for low-cost hardware implementation. The proposed VLSI architecture employs multiple (frame, pixel, and operation) levels of pipeline and massively parallel processing arrays to boost the system performance. The array unit circuits are optimized to minimize hardware resource consumption. We have prototyped the proposed architecture on a low-cost field-programmable gate array platform (Zynq 7020) running at 53-MHz clock frequency. It achieved 30-frame/s real-time performance for velocity estimation on 160 × 120 probing locations. A comprehensive evaluation experiment showed that the estimated velocity by our prototype has relatively small errors (average endpoint error < 0.5 pixel and angular error < 10°) for most motion cases.

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“…In most of the tracking algorithm, the tracking performance depends up on the target image representation. Colour, texture and edge are typical attributes used for representing objects [20], [21] beyond this SIFT feature [22], optical flow [23], orientation energy [5] and active contours [11] are also used in many of the works. [25] it was revealed that the visual perception of animals relies heavily on the directional edges so that they are having excellent ability in visual tracking.…”

Section: Introductionmentioning

confidence: 99%

Real Time Implementation of Object Tracking Through Webcam

Cherian¹

2014

IJRET

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Real time object detection and tracking is an important task in various computer vision applications. For robust object tracking the factors like object shape variation, partial and full occlusion, scene illumination variation will create significant problems. We introduce object detection and tracking approach that combines Prewitt edge detection and kalman filter. The target object's representation and the location prediction are the two major aspects for object tracking this can be achieved by using these algorithms. Here real time object tracking is developed through webcam. Experiments show that our tracking algorithm can track moving object efficiently under object deformation, occlusion and can track multiple objects.

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Robust object tracking using local oriented energy features and its hardware/software implementation

Cited by 10 publications

References 15 publications

Implementation of real time moving object detection using background subtraction in FPGA

Implementation of real time moving object detection using background subtraction in FPGA

A Compact VLSI System for Bio-Inspired Visual Motion Estimation

Real Time Implementation of Object Tracking Through Webcam

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