Robust hand detection in Vehicles

The problems of hand detection have been widely addressed in many areas, e.g. human computer interaction environment, driver behaviors monitoring, etc. However, the detection accuracy in recent hand detection systems are still far away from the demands in practice due to a number of challenges, e.g. hand variations, highly occlusions, low-resolution and strong lighting conditions. This paper presents the Multiple Scale Faster Region-based Convolutional Neural Network (MS-FRCNN) to handle the problems of hand detection in given digital images collected under challenging conditions. Our proposed method introduces a multiple scale deep feature extraction approach in order to handle the challenging factors to provide a robust hand detection algorithm. The method is evaluated on the challenging hand database, i.e. the Vision for Intelligent Vehicles and Applications (VIVA) Challenge, and compared against various recent hand detection methods. Our proposed method achieves the state-of-the-art results with 20% of the detection accuracy higher than the second best one in the VIVA challenge.

[1]  Andrew Zisserman,et al.  Hand detection using multiple proposals , 2011, BMVC.

[2]  Chen Yu,et al.  Viewpoint Integration for Hand-Based Recognition of Social Interactions from a First-Person View , 2015, ICMI.

[3]  Mohan M. Trivedi,et al.  Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-Based Approach and Evaluations , 2014, IEEE Transactions on Intelligent Transportation Systems.

[4]  Chen Qian,et al.  Realtime and Robust Hand Tracking from Depth , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Jonathan T. Barron,et al.  Multiscale Combinatorial Grouping , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Mohan M. Trivedi,et al.  On Performance Evaluation of Driver Hand Detection Algorithms: Challenges, Dataset, and Metrics , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[9]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[10]  Mohan M. Trivedi,et al.  Head, Eye, and Hand Patterns for Driver Activity Recognition , 2014, 2014 22nd International Conference on Pattern Recognition.

[11]  Jian Sun,et al.  Cascaded hand pose regression , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Wei Liu,et al.  ParseNet: Looking Wider to See Better , 2015, ArXiv.

[13]  Antti Oulasvirta,et al.  Fast and robust hand tracking using detection-guided optimization , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).