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Real-time Pedestrian Detection with Deformable Part Models
Any type of content formally published in an academic journal, usually following a peer-review process.
We describe a real-time pedestrian detection system intended for use in automotive applications. Our system demonstrates superior detection performance when compared to many state-of-the-art detectors and is able to run at a speed of 14 fps on an Intel Core i7 computer when applied to 640×480 images. Our approach uses an analysis of geometric constraints to efficiently search feature pyramids and increases detection accuracy by using a multiresolution representation of a pedestrian model to detect small pixel-sized pedestrians normally missed by a single representation approach. We have evaluated our system on the Caltech Pedestrian benchmark which is currently the largest publicly available pedestrian dataset at the time of this publication. Our system shows a detection rate of 61% with 1 false positive per image (FPPI) whereas recent other state-of-the-art detectors show a detection rate of 50% ~ 61% under the `reasonable' test scenario (explained later). Furthermore, we also demonstrate the practicality of our system by conducting a series of use case experiments on selected videos of Caltech dataset.