A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
A trainable system for object detection in images and video sequences
A trainable system for object detection in images and video sequences
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Probabilistic Kernels for the Classification of Auto-Regressive Visual Processes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A feature-based tracking algorithm for vehicles in intersections
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
3D Urban Scene Modeling Integrating Recognition and Reconstruction
International Journal of Computer Vision
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Robust Technique for Background Subtraction and Shadow Elimination in Traffic Video Sequence
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-scale vehicle detection in challenging urban surveillance environments
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient use of geometric constraints for sliding-window object detection in video
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Interpolation Based Tracking for Fast Object Detection in Videos
NCVPRIPG '11 Proceedings of the 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Reconfigurable templates for robust vehicle detection and classification
WACV '12 Proceedings of the 2012 IEEE Workshop on the Applications of Computer Vision
Teaching 3D geometry to deformable part models
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Vehicle Detection Using Mixture of Deformable Parts Models: Static and Dynamic Camera
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
Efficiently Scaling up Crowdsourced Video Annotation
International Journal of Computer Vision
Vision meets robotics: The KITTI dataset
International Journal of Robotics Research
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Sequences of images from urban environments are increasing in number as well as their potential applications. They are being taken (from stationary and dynamic cameras) for applications such as traffic surveillance, or for autonomous driving, or for security applications, etc. The literature presents several different approaches for each application. For object detection, a common disadvantage is that they only consider images obtained from a stationary, or a dynamic, camera to train the detectors. This can lead to poor performances when the detectors are used in sequences of images from different types of cameras, or even a cross camera testing. e.g., training with data from a dynamic camera and testing with sequences from a stationary camera. Another disadvantage is that some approaches use several models for each point of view of the car, generating a lot of models and, in some cases, one classifier for each point of view. In this paper, we approach the problem of car detection using a model of the class car created with a dataset of static images and we use the model to detect cars in sequence of images that were collected from static and dynamic cameras, i.e., in a totally different setting than used for training. The creation of the model is done by an off-line learning phase, using an image database of cars in several points of view, PASCAL 2007. The model is based on a mixture of deformable part models that have been shown to give state of the art results for detection in static images. The results show that the proposed approach achieves better results than the state of the art approaches in sequence of images obtained from a stationary, or a dynamic camera. Another contribution of our paper is a ground truth of a large sequence of images available in the Internet.