Parallel object detection on multicore platforms

Object detection is an important function for intelligent multimedia processing, but its computational complexity prevented its pervasive uses in consumer electronics. Cost-effective & energy-efficient computations are now available with various innovative multicore architectures proposed for embedded systems. However, extensive software optimizations are needed to unravel the inherent parallelisms in object detection for multicore processing. This paper presents interleaved reordering and splitting of parallel tasks in object detection. Overall performance improvements by 10% & 19% have been measured for the proposed methods respectively on a face detection prototype implemented on Sony PlayStation 3.

[1]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Ryusuke Miyamoto,et al.  A Real-Time Object Recognition System on Cell Broadband Engine , 2007, PSIVT.

[4]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  H. Peter Hofstee,et al.  Introduction to the Cell multiprocessor , 2005, IBM J. Res. Dev..

[6]  M. Suzuoki,et al.  Overview of the architecture, circuit design, and physical implementation of a first-generation cell processor , 2006, IEEE Journal of Solid-State Circuits.

[7]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).