A Robust Pedestrian Detection Approach Based on Shapelet Feature and Haar Detector Ensembles Wentao Yao, Zhidong Deng TSINGHUA SCIENCE AND TECHNOLOGY ISSNl l l l04/12l lpp40-50 Volume 17, Number 1, February

OUTLINE Introduction Proposed Algorithm – Improved Full Body Shapelet Pedestrian Detector – Ensemble Approach for Pedestrian Part Detectors Experimental Results Conclusions and Future Work 2

OUTLINE Introduction Proposed Algorithm – Improved Full Body Shapelet Pedestrian Detector – Ensemble Approach for Pedestrian Part Detectors Experimental Results Conclusions and Future Work 3

Introduction Example-based methods for pedestrian detection use one or more detectors trained on a large number of samples that contain both positives and negatives, with the detectors then applied to the images during the detection stage. Full body detectors can achieve a relatively high detection rate, but they do not deal well with various poses and pedestrian occlusions. Part-based detection methods have become more popular. 4

OUTLINE Introduction Proposed Algorithm – Improved Full Body Shapelet Pedestrian Detector – Ensemble Approach for Pedestrian Part Detectors Experimental Results Conclusions and Future Work 5

Proposed Algorithm This paper describes a two-stage detection approach that combines both full body and part-based detectors. The first stage uses a full body detector based on shapelet features to generate pedestrian candidates. The second stage uses part detectors based on Haar- like wavelet features to verify the full body candidates. 6

OUTLINE Introduction Proposed Algorithm – Improved Full Body Shapelet Pedestrian Detector – Ensemble Approach for Pedestrian Part Detectors Experimental Results Conclusions and Future Work 7

Improved Full Body Shapelet Pedestrian Detector [17] Sabzmeydani P, Mori G. Detecting pedestrians by learning shapelet features. In: IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA,

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OUTLINE Introduction Proposed Algorithm – Improved Full Body Shapelet Pedestrian Detector – Ensemble Approach for Pedestrian Part Detectors Experimental Results Conclusions and Future Work 11

Ensemble Approach for Pedestrian Part Detectors Human parts segmentation and part detectors training Part detector ensemble Genetic algorithm search for the optimal labeling state 12

Human Parts Segmentation and Part Detectors Training [15] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition. Kauai, HI, USA, 2001:

Part Detector Ensemble [22] Dai S Y, Yang M, Wu Y, et al. Detector ensemble. In: IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA,

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[22] Dai S Y, Yang M, Wu Y, et al. Detector ensemble. In: IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA,

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Genetic Algorithm Search For the Optimal Labeling State [26] Comaniciu D. Nonparametric information fusion for motion estimation. In: IEEE Conference on Computer Vision and Pattern Recognition. Madison, WI, USA, 2003: [27] Guo H M, Guo P, Lu H Q. A fast mean shift procedure with new iteration strategy and re-sampling. In: IEEE International Conference on Systems, Man and Cybernetics. Taipei, Taiwan, 2006:

OUTLINE Introduction Proposed Algorithm – Improved Full Body Shapelet Pedestrian Detector – Ensemble Approach for Pedestrian Part Detectors Experimental Results Conclusions and Future Work 19

Experimental Results 20

Tests of the Full Body Shapelet Detector (2,4,6) indicates that the sliding step sizes for sub-windows with 5×5, 10×10, and 15×15 pixels were 2, 4, and 6 pixels along both the x and y directions. 21

Pedestrian Detection System Test Results 22

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S stands for shapelet, H for Haar, and FB for full body. 24

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OUTLINE Introduction Proposed Algorithm – Improved Full Body Shapelet Pedestrian Detector – Ensemble Approach for Pedestrian Part Detectors Experimental Results Conclusions and Future Work 26

Conclusions and Future Work The two-stage detection approach eliminates most non- pedestrian samples in the first stage. Five part detectors and a detector ensemble are trained to verify the pedestrian candidates generated by the first stage. After the verification process, neighboring detection results are partitioned into equivalency classes and merged together. More features, such as color and texture, can be used to further improve the system performance. 27