IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2003 Papers
Robust Real-Time Face Detection
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Edge-Based Rich Representation for Vehicle Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A detector tree of boosted classifiers for real-time object detection and tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Parallelizing AdaBoost by weights dynamics
Computational Statistics & Data Analysis
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Large-scale vehicle detection in challenging urban surveillance environments
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
License Plate Recognition From Still Images and Video Sequences: A Survey
IEEE Transactions on Intelligent Transportation Systems
Attribute learning in large-scale datasets
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Practical computer vision: example techniques and challenges
IBM Journal of Research and Development
Proceedings of the 20th ACM international conference on Multimedia
Vehicle re-identification collaborating visual and temporal-spatial network
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
| Hi-index | 0.00 |
We present a novel application for searching for vehicles in surveillance videos based on semantic attributes. At the interface, the user specifies a set of vehicle characteristics (such as color, direction of travel, speed, length, height, etc.) and the system automatically retrieves video events that match the provided description. A key differentiating aspect of our system is the ability to handle challenging urban conditions such as high volumes of activity and environmental factors. This is achieved through a novel multi-view vehicle detection approach which relies on what we call motionlet classifiers, i.e. classifiers that are learned with vehicle samples clustered in the motion configuration space. We employ massively parallel feature selection to learn compact and accurate motionlet detectors. Moreover, in order to deal with different vehicle types (buses, trucks, SUVs, cars), we learn the motionlet detectors in a shape-free appearance space, where all training samples are resized to the same aspect ratio, and then during test time the aspect ratio of the sliding window is changed to allow the detection of different vehicle types. Once a vehicle is detected and tracked over the video, fine-grained attributes are extracted and ingested into a database to allow future search queries such as "Show me all blue trucks larger than 7ft length traveling at high speed northbound last Saturday, from 2pm to 5pm".