This paper presents 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 ofrejection type classifier is utilized for our pedestrian detection system. Initial stage of cascade, simple rule based classification techniques 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.
Samenvatting