PHACT: Parallel HOG and Correlation Tracking
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چکیده
Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self learning to overcome the drifting problem. A detection algorithm that utilises HOG features runs in parallel to an adaptive and stateful correlator. The combination of both acting in a cascade provides a much more robust tracker than the two components separately could produce.
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Human tracking with multiple parallel metrics
The tracking of humans in a video stream has become one of the most desirable computer vision tasks over the past few years. It remains however a difficult problem and the reliability of systems is often dependent on getting good lighting and clear video images. This paper reports on the development of our PHACT tracker: parallel HOG and correlation tracking. This system uses a cascade of track...
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