Pedestrian tracking is a critical problem in the field of computer vision. Pedestrian detection in images could be used in video surveillance systems and driver assistance systems. It is more challenging to detect moving objects or pedestrian in order to avoid an obstacle and control locomotion of the vehicle in the real-world environment. This paper presents a pedestrian detection method from a moving vehicle using optical flows and Histogram of Oriented Gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using firefly-based Support Vector Machine (SVM). The HOG feature vectors are used as input of firefly-based SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset.
Volume 11 | Issue 7
Pages: 349-354