Pedestrian detection by on-board camera using collaboration of inter-layer algorithm
Bipul Kumar Sen, Kaichi Fujimura, Shunsuke Kamijo · 2009
In this paper we present a robust pedestrian detection algorithm in low resolution on-board monocular camera image sequences of cluttered scenes. At first a motion based object detection algorithm is developed to detect foreground objects by analyzing horizontal motion vector. A cascade structure of rejection type classifier is utilized for our pedestrian detection system. Initial stage of cascade, simple rule based classification techniques are used to separate pedestrian from obvious road side structural object and later part of the cascade, a more complex algorithm which is a combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) based classification techniques are utilized to separate pedestrian from non-pedestrian objects. Finally, the image segments are tracked by our Spatio-Temporal Markov Random Field model (S-T MRF). Results show that our algorithms are promising for pedestrian detection in cluttered scenes.