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In the context of driving assistance, detection of moving targets is challenging when the automobile is witnessing very complex environment. The main challenges come from difficulties to tell foreground from moving background due to camera motion. We present an efficient method for target detection by hierarchically distinguishing the motion of targets from that of the background and using color information to help with the process. Firstly, point features are extracted and tracked to form feature trajectories. Secondly, distance measures are defined on the trajectories and hierarchically used to cluster the generated trajectories into regions. The last step is devoted to better detection of the targets by adding appearance information. Experiments show our method is able to give target detection results in real time even in very complex environments.

An intelligent vehicle system is deeloped, where five single-row laser scannners are mounted on the car to profile object geometry along the streets from different viewpoints and with different directions; a GPS (Global Positioning System)/IMU (Inertial Measurement Unit) based navigation unit is also applied to give outputs of vehicle pose. This paper focus on sensor alignment in order to achieve an omni-directional laser measurement of dynamic urban environment using an intelligent vehicle. An algorithm is proposed, where the laser scanners that facing towards different directions are aligned through horizontal and vertical matching of laser points. Experimental results are presented demonstrating that a comprehensive knowledge of the environment can be achieved, and shortages of each individual sensor can be compensated through such a sensor fusion.