Embedded multi-sensors objects detection and tracking for urban autonomous driving

This paper proposes an embedded real time method for detecting and tracking of multiobjects including vehicles, pedestrians, motorbikes and bicycles in urban environment. The features of different objects are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOG). Laser depth data have been used as a priori to generate objects hypothesis regions and estimate HOG feature pyramid level to reduce the detection time of previously presented algorithm. Detected objects are tracked through a particle filter which fuses the observations from laser map and sequential images. We use the accurate laser data for state predication and use image HOG information for likelihood calculation. The likelihood finds the maximum HOG feature compatibility for both root and parts of the tracked objects to increase tracking accuracy for deformable objects such as pedestrians in crowded scenes. Extensive experiments with urban scenarios showed that the proposed method can improve the detection and tracking in urban environment.