A chest-shape target automatic detection method based on Deformable  Part Models

Mo Zhang; Weiqi Jin; Li Li

doi:10.1117/12.2245832

31 October 2016 A chest-shape target automatic detection method based on Deformable Part Models

Mo Zhang, Weiqi Jin, Li Li

Proceedings Volume 10020, Optoelectronic Imaging and Multimedia Technology IV; 1002010 (2016) https://doi.org/10.1117/12.2245832
Event: SPIE/COS Photonics Asia, 2016, Beijing, China

Abstract

Automatic weapon platform is one of the important research directions at domestic and overseas, it needs to accomplish fast searching for the object to be shot under complex background. Therefore, fast detection for given target is the foundation of further task. Considering that chest-shape target is common target of shoot practice, this paper treats chestshape target as the target and studies target automatic detection method based on Deformable Part Models. The algorithm computes Histograms of Oriented Gradient(HOG) features of the target and trains a model using Latent variable Support Vector Machine(SVM); In this model, target image is divided into several parts then we can obtain foot filter and part filters; Finally, the algorithm detects the target at the HOG features pyramid with method of sliding window. The running time of extracting HOG pyramid with lookup table can be shorten by 36%. The result indicates that this algorithm can detect the chest-shape target in natural environments indoors or outdoors. The true positive rate of detection reaches 76% with many hard samples, and the false positive rate approaches 0. Running on a PC (Intel(R)Core(TM) i5-4200H CPU) with C++ language, the detection time of images with the resolution of 640 × 480 is 2.093s. According to TI company run library about image pyramid and convolution for DM642 and other hardware, our detection algorithm is expected to be implemented on hardware platform, and it has application prospect in actual system.

Citation Download Citation

Mo Zhang, Weiqi Jin, and Li Li "A chest-shape target automatic detection method based on Deformable Part Models", Proc. SPIE 10020, Optoelectronic Imaging and Multimedia Technology IV, 1002010 (31 October 2016); https://doi.org/10.1117/12.2245832

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