Speeding up Adaboost object detection with motion segmentation and Haar feature acceleration

Abstract

A key challenge in a surveillance system is the object detection task. Object detection in general is a non-trivial problem. A sub-problem within the broader context of object detection which many researchers focus on is face detection. Numerous techniques have been proposed for face detection. One of the better performing algorithms is proposed by Viola et. al. This algorithm is based on Adaboost and uses Haar features to detect objects. The main reason for its popularity is very low false positive rates and the fact that the classifier network can be trained for any detection task. The use of Haar basis functions to represent key object features is the key to its success. The basis functions are organized as a network to form a strong classifier. To detect objects, this technique divides each input image into non-overlapping sub-windows and the strong classifier is applied to each sub-window to detect the presence of an object. The process is repeated at multiple scales of the input image to detect objects of various sizes. In this thesis we propose an object detection system that uses object segmentation as a preprocessing step. We use Mixture of Gaussians (MoG) proposed by Staffer et. al. for object segmentation. One key advantage with using segmentation to extract image regions of interest is that it reduces the number of search windows sent to detection task, thereby reducing the computational complexity and the execution time. Moreover, owing to the computational complexity of both the segmentation and detection algorithms we used in the system, we propose hardware architectures for accelerating key computationally intensive blocks. In this thesis we propose hardware architecture for MoG and also for a key compute intensive block within the adaboost algorithm corresponding to the Haar feature computation