Crosstalk Cascades for Frame-Rate Pedestrian Detection

Piotr Dollár¹, Ron Appel², and Wolf Kienzle¹

¹Microsoft Research, Redmond, USA
pdollar@microsoft.com
wkienzle@microsoft.com

²California Institute of Technology, USA
appel@caltech.edu

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 (4-30× 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 640×480 images while attaining state-of-the-art accuracy.

LNCS 7573, p. 645 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012