Crosstalk Cascades for Frame-Rate Pedestrian Detection

P. Dollár, R. Appel, and W. Kienzle

Abstract

Cascades help make sliding window object detection fast, nevertheless, computational demands remain prohibitive for numerous applications. Currently, evaluation of adjacent windows proceeds independently; this is suboptimal as detector responses at nearby locations and scales are correlated. We propose to exploit these correlations by tightly coupling detector evaluation of nearby windows. We introduce two opposing mechanisms: detector excitation of promising neighbors and inhibition of inferior neighbors. By enabling neighboring detectors to communicate, crosstalk cascades achieve major gains (4-30x speedup) over cascades evaluated independently at each image location. Combined with recent advances in fast multi-scale feature computation, for which we provide an optimized implementation, our approach runs at 35-65 fps on 640x480 images while attaining state-of-the-art accuracy.

Details

Publication typeInproceedings
Published inECCV
URLhttp://vision.ucsd.edu/~pdollar/research.html
PublisherEuropean Conference on Computer Vision
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