Real-time face detection and tracking for mobile videoconferencing

Paschalakis, Stavros; Bober, Miroslaw

doi:10.1016/j.rti.2004.02.004

Cited by 25 publications

(15 citation statements)

References 32 publications

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“…The process is repeated for 32 blocks in a loop fashion so that the error gets totally eliminated or reduced to a very small amount. The resulting output pixel value represents the edges of the overall image [6].…”

Section: A Training Of Neural Networkmentioning

confidence: 99%

Edge Detection System using Pulse Mode Neural Network for Image Enhancement

Babu¹,

Karunakaran²,

Venkatraman³

et al. 2012

IJIGSP

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-Edge detection of an image reduces significantly the amount of data and filters out information that may be regarded as less irrelevant. Edge detection is efficient in medical imaging. Pulse mode neural networks are becoming an attractive solution for function approximation based on frequency modulation. Early pulse mode implementation suffers from some network constraints due to weight range limitations. To provide the best edge detection, the basic algorithm is modified to have pulse mode operations for effective hardware implementation. In this project a new pulse mode network architecture using floating point operations is used in the activation function. By using floating point number system for synapse weight value representation, any function can be approximated by the network. The proposed pulse mode MNN is used to detect the edges in images forming a heterogeneous data base. It shows good learning capability. In addition, four edge detection techniques have been compared. The coding is written in verilog and the final result have been simulated using Xilinx ISE simulator.

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Section: A Training Of Neural Networkmentioning

confidence: 99%

Edge Detection System using Pulse Mode Neural Network for Image Enhancement

Babu¹,

Karunakaran²,

Venkatraman³

et al. 2012

IJIGSP

View full text Add to dashboard Cite

show abstract

“…The importance of visual object tracking comes from the fact that it has a wide range of real-world applications, including surveillance and monitoring of human activities in residential areas (parking lots and banks) [2,3], smart rooms [4,5], traffic flow monitoring [6], traffic accident detection [7], hand gesture recognition [8], mobile video conferencing [9], video compression [10,11] and enabling an unmanned ground vehicle to handle a road intersection [12].…”

Section: Introductionmentioning

confidence: 99%

Real-Time FPGA-Based Object Tracker with Automatic Pan-Tilt Features for Smart Video Surveillance Systems

2017

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Abstract:The design of smart video surveillance systems is an active research field among the computer vision community because of their ability to perform automatic scene analysis by selecting and tracking the objects of interest. In this paper, we present the design and implementation of an FPGA-based standalone working prototype system for real-time tracking of an object of interest in live video streams for such systems. In addition to real-time tracking of the object of interest, the implemented system is also capable of providing purposive automatic camera movement (pan-tilt) in the direction determined by movement of the tracked object. The complete system, including camera interface, DDR2 external memory interface controller, designed object tracking VLSI architecture, camera movement controller and display interface, has been implemented on the Xilinx ML510 (Virtex-5 FX130T) FPGA Board. Our proposed, designed and implemented system robustly tracks the target object present in the scene in real time for standard PAL (720 × 576) resolution color video and automatically controls camera movement in the direction determined by the movement of the tracked object.

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“…Visual object tracking is one of the exigent missions in computer vision community and has a wide range of realworld applications, including surveillance and monitoring of human activities in residential areas, parking lots, and banks [1]- [2], smart rooms [3]- [4], traffic flow monitoring [5], mobile video conferencing [6], and video compression [7]- [8]. Different issues arising in development of a real-time object tracking system for such applications are because of camera movement, complex object motion, presence of other moving objects in video scene, and real-time processing requirements.…”

Section: Introductionmentioning

confidence: 99%

Real-time Object Tracking with Active PTZ Camera using Hardware Acceleration Approach

Singh¹,

Saini²,

Saurav³

et al. 2017

IJIGSP

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Abstract-This paper presents the design and implementation of a dedicated hardware (VLSI) architecture for real-time object tracking. In order to realize the complete system, the designed VLSI architecture has been integrated with different input/output video interfaces. These video interfaces along with the designed object tracking VLSI architecture have been coded using VHDL, simulated using ModelSim, and synthesized using Xilinx ISE tool chain. A working prototype of complete object tracking system has been implemented on Xilinx ML510 (Virtex-5 FX130T) FPGA board. The implemented system is capable of tracking the moving target object in real-time in PAL (720x576) resolution live video stream directly coming from the camera. Additionally, the implemented system also provides the real-time desired camera movement to follow the tracked object over a larger area.

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Real-time face detection and tracking for mobile videoconferencing

Cited by 25 publications

References 32 publications

Edge Detection System using Pulse Mode Neural Network for Image Enhancement

Edge Detection System using Pulse Mode Neural Network for Image Enhancement

Real-Time FPGA-Based Object Tracker with Automatic Pan-Tilt Features for Smart Video Surveillance Systems

Real-time Object Tracking with Active PTZ Camera using Hardware Acceleration Approach

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