Abstract
Nowadays pedestrian detection plays an important role in security and driving assistance. Detecting moving object is complex, and some of the detection methods are comparatively ineffective and slow. In relation to human detection it is very useful to combine independent information sources, such as appearance and motion. To achieve acceptable detection performance, we propose inter-frames differencing image to compute the region of interest, and MB-BIF to extract features. The MB-BIF approach combines two well-known methods, the Multi-Block Local Binary Pattern and Biologically Inspired Method. We evaluate the performance of different features descriptors on different databases, and our method shows good efficiency.
Highlights
In recent years, there has been a great progress in detection systems
To achieve acceptable detection performance, we propose inter-frames differencing image to compute the region of interest, and Multi-block Bio-Inspired Features (MB-BIF) to extract features
In order to solve the problems encountered in standard moving object detection methods we proposed a Fast determination of region of interest (ROI) and MB-BIF features
Summary
There has been a great progress in detection systems. Lot of efforts have been made in the development of pedestrian detection systems, for its importance in practical applications such as, driving safety and surveillance systems. In general the motion detection is based on three approaches (Radke, Andra, Al-Kofahi, & Roysam, 2005), known as optical flow (Velastin, Boghossian, Lo, Sun, & Vicencio-Silva, 2005) based on the intensity change of the moving objects, background subtraction (Piccardi, 2004) based on the difference between the given frame and the background model, frame differencing (Kim & Hwang, 2002) based on simple consecutive frames difference. These approaches treat pedestrian detection as two categories: human and non-human classification problems.
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