Fast Pedestrian Detection for Mobile Devices

In this paper we present a fast and robust solution for pedestrian detection that can run in real time conditions even on mobile devices with limited computational power. An optimization of the channel features based multiscale detection schemes is proposed by using 8 detection models for each half octave scales. The image features have to be computed only once each half octave and there is no need for feature approximation. We use multiscale square features for training the multiresolution pedestrian classifiers. The proposed solution achieves state of art detection results on Caltech pedestrian benchmark at over 100 FPS using a CPU implementation, being the fastest detection approach on the benchmark. The solution is fast enough to perform under real time conditions on mobile platforms, yet preserving its robustness. The full detection process can run at over 20 FPS on a quad-core ARM CPU based smartphone or tablet, being a suitable solution for limited computational power mobile devices or embedded platforms.

[1]  Xiaogang Wang,et al.  Multi-stage Contextual Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[2]  Arthur Daniel Costea,et al.  Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Jing Xiao,et al.  Detection Evolution with Multi-order Contextual Co-occurrence , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Xiaogang Wang,et al.  Switchable Deep Network for Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  David Gerónimo Gómez,et al.  Survey of Pedestrian Detection for Advanced Driver Assistance Systems , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Sergiu Nedevschi,et al.  Real-time pedestrian detection in urban scenarios , 2014, 2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP).

[10]  Anton van den Hengel,et al.  Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Deva Ramanan,et al.  Exploring Weak Stabilization for Motion Feature Extraction , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Luc Van Gool,et al.  Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Charless C. Fowlkes,et al.  Multiresolution Models for Object Detection , 2010, ECCV.

[14]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[15]  Joon Hee Han,et al.  Local Decorrelation For Improved Pedestrian Detection , 2014, NIPS.

[16]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[17]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Piotr Dollár,et al.  Crosstalk Cascades for Frame-Rate Pedestrian Detection , 2012, ECCV.

[19]  Xiaogang Wang,et al.  Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[20]  Pietro Perona,et al.  The Fastest Pedestrian Detector in the West , 2010, BMVC.

[21]  Armin B. Cremers,et al.  Informed Haar-Like Features Improve Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Shengcai Liao,et al.  Robust Multi-resolution Pedestrian Detection in Traffic Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.