Multiple human tracking in high-density crowds
Image and Vision Computing
Improving HOG with image segmentation: application to human detection
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Crosstalk cascades for frame-rate pedestrian detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
An introduction to random forests for multi-class object detection
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
An study on re-identification in RGB-D imagery
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection
Engineering Applications of Artificial Intelligence
Fusing LIDAR, camera and semantic information: A context-based approach for pedestrian detection
International Journal of Robotics Research
Multiple instance classification: Review, taxonomy and comparative study
Artificial Intelligence
A novel multiplex cascade classifier for pedestrian detection
Pattern Recognition Letters
Object class detection: A survey
ACM Computing Surveys (CSUR)
Multi-target tracking on confidence maps: An application to people tracking
Computer Vision and Image Understanding
Automatic unconstrained online configuration of a master-slave camera system
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Detecting pedestrians on a Movement Feature Space
Pattern Recognition
Easy, fast, and energy-efficient object detection on heterogeneous on-chip architectures
ACM Transactions on Architecture and Code Optimization (TACO)
Macrofeature layout selection for pedestrian localization and its acceleration using GPU
Computer Vision and Image Understanding
Pedestrian detection in far infrared images
Integrated Computer-Aided Engineering
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Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detecting pedestrians in monocular images has grown steadily. However, multiple data sets and widely varying evaluation protocols are used, making direct comparisons difficult. To address these shortcomings, we perform an extensive evaluation of the state of the art in a unified framework. We make three primary contributions: 1) We put together a large, well-annotated, and realistic monocular pedestrian detection data set and study the statistics of the size, position, and occlusion patterns of pedestrians in urban scenes, 2) we propose a refined per-frame evaluation methodology that allows us to carry out probing and informative comparisons, including measuring performance in relation to scale and occlusion, and 3) we evaluate the performance of sixteen pretrained state-of-the-art detectors across six data sets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. Our experiments show that despite significant progress, performance still has much room for improvement. In particular, detection is disappointing at low resolutions and for partially occluded pedestrians.