Trainable pedestrian detection

Robust, fast object detection systems are critical to the success of next-generation automotive vision systems. An important criteria is that the detection system be easily configurable to a new domain or environment. In this paper, we present work on a general object detection system that can be trained to detect different types of objects; we focus on the task of pedestrian detection. This paradigm of learning from examples allows us to avoid the need for a hand-crafted solution. Unlike many pedestrian detection systems, the core technique does not rely on motion information and makes no assumptions on the scene structured or the number of objects present. We discuss an extension to the system that takes advantage of dynamical information when processing video sequences to enhance accuracy. We also describe a real, real-time version of the system that has been integrated into a DaimlerChrysler test vehicle.

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