Heuristic Based Labeling Using Edgelet Based Contour Detection for Low Resolution Small to Medium Scaled Monocular Pedestrian Detection

Abstract

Outdoor pedestrian detection is one of most important, primary and challenging preprocessing step for any automated visual surveillance activity ranging from event to activity detection. Currently used algorithms are resolution, scale and clothing dependent, their performance decreases as resolution of camera decreases and the size of pedestrian decreases to small scale (30-80 pixels). Moreover, regions like Middle East where loose clothes are mostly used the performance of these systems get worse. The reason behind the failure of these systems is that most of them target the contour information of pedestrian and as the scale decreases or cloth varies the contour information becomes ambiguous. Noise accumulation, wide area view, low resolution, outdoor environment artifacts, low frame rate further add to the complexity of pedestrian detection. The paper proposes a pedestrian detection algorithm that detects the pedestrian of small scale from the low frame rate (5 frames per second) video captured by low resolution CCTV camera (352x288) resolution. The algorithm is clothing and illumination invariant and proposes two main contributions: first, motion cues and edgelets based contour detection (ECD) are used to target temporal and low level pixel details, handling clothing variation and providing a heuristic window for pedestrian detection and second, the heuristic window is searched for presence of pedestrian using linear support vector machine (SVM)classifier trained over hybrid of histogram of oriented gradients (HOG) and statistical shape based feature vector.