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Real-time DSP implementation of Pedestrian Detection algorithm using HOG features
Pedestrian Detection is the most critical safety application in automotive driver assistance systems. Histogram of Oriented Gradients (HOG) features is known to produce the state of the art results for this application. This feature is very compute-intensive and it is difficult to achieve real-time performance by direct porting of community software like OpenCV. In this paper, we discuss an efficient DSP implementation of this algorithm and also demonstrate how architecture aware design choices can lead to huge performance improvements. The algorithm was implemented and profiled on a Texas Instruments' C674x DSP, achieving a performance of 20 fps for a VGA resolution video sequence. Compared to OpenCV's HOG function, the proposed implementation is 130X faster without a significant loss of accuracy.
Real-time DSP implementation of Pedestrian Detection algorithm using HOG features
Pedestrian Detection is the most critical safety application in automotive driver assistance systems. Histogram of Oriented Gradients (HOG) features is known to produce the state of the art results for this application. This feature is very compute-intensive and it is difficult to achieve real-time performance by direct porting of community software like OpenCV. In this paper, we discuss an efficient DSP implementation of this algorithm and also demonstrate how architecture aware design choices can lead to huge performance improvements. The algorithm was implemented and profiled on a Texas Instruments' C674x DSP, achieving a performance of 20 fps for a VGA resolution video sequence. Compared to OpenCV's HOG function, the proposed implementation is 130X faster without a significant loss of accuracy.
Real-time DSP implementation of Pedestrian Detection algorithm using HOG features
Chavan, Akshay (author) / Yogamani, Senthil Kumar (author)
2012-11-01
332535 byte
Conference paper
Electronic Resource
English
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