International audienceObjects recognition in image is one of the most difficult problems in computer vision. It is also an important step for the implementation of several existing applications that require high-level image interpretation. Therefore, there is a growing interest in this research area during the last years. In this paper, we present an algorithm for human detection and recognition in real-time, from images taken by a CCD camera mounted on a car-like mobile robot. The proposed technique is based on Histograms of Oriented Gradient (HOG) and SVM classifier. The implementation of our detector has provided good results, and can be used in robotics tasks

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A necessary condition to perform a fully autonomous driving system in urban environment is to detect object types in real scenes. Visual object recognition is a key solution, but multi-object detection still remain unsolved. In this paper, we present a fast and efficient multi-object detection system built to recognize, at the same time, pedestrians cars and bicycles. For each target type, we construct a holistic detector in a cascade manner, using a dense overlapping grid based on histograms of oriented gradients (HOG). The selection of HOG features is obtained through a learning process using AdaBoost algorithm. Experiments have been conducted on the car-like robot Robucar, where the single detectors are combined and implemented on its embedded computer, which is endowed with a modular software platform. Results are promising as the system can process up to 20 fps with VGA images.

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