A Unified Framework for Concurrent Pedestrian and Cyclist Detection
- 7 July 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Intelligent Transportation Systems
- Vol. 18 (2), 269-281
- https://doi.org/10.1109/tits.2016.2567418
Abstract
Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.Keywords
Funding Information
- National Natural Science Foundation of China (51475254)
- Tsinghua University
- Daimler
This publication has 26 references indexed in Scilit:
- ImageNet classification with deep convolutional neural networksCommunications of the ACM, 2017
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- A Probabilistic Framework for Joint Pedestrian Head and Body Orientation EstimationIEEE Transactions on Intelligent Transportation Systems, 2015
- Selective Search for Object RecognitionInternational Journal of Computer Vision, 2013
- A new benchmark for stereo-based pedestrian detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Object Detection with Discriminatively Trained Part-Based ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2009
- The Pascal Visual Object Classes (VOC) ChallengeInternational Journal of Computer Vision, 2009
- Integral Channel FeaturesPublished by British Machine Vision Association and Society for Pattern Recognition ,2009
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Detecting pedestrians using patterns of motion and appearancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003