Segmentation of ultrasonic images using support vector machines
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Evaluating Image Segmentation Algorithms Using the Pareto Front
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Temporal Network of Support Vector Machine Classifiers for the Recognition of Visual Speech
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
Detecting motion patterns via direction maps with application to surveillance
Computer Vision and Image Understanding
Multiclass object classification for real-time video surveillance systems
Pattern Recognition Letters
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Current systems for object detection in video sequences rely on explicit dynamical models like Kalman filters or hidden Markov models. There is significant overhead needed in the development of such systems as well as the a priori assumption that the object dynamics can be described with such a dynamical model. This paper describes a new pattern classification technique for object detection in video sequences that uses a rich, over complete dictionary of wavelet features to describe an object class. Unlike previous work where a small subset of features was selected from the dictionary, this system does no feature selection and learns the model in the full 1,326 dimensional feature space. Comparisons using different sized sets of several types of features are given. We extend this representation into the time domain without assuming any explicit model of dynamics. This data driven approach produces a model of the physical structure and short-time dynamical characteristics of people from a training set of examples; no assumptions are made about the motion of people, just that short sequences characterize their dynamics sufficiently for the purposes of detection. One of the main benefits of this approach is that transient false positives are reduced. This technique compares favorably with the static detection approach and could be applied to other object classes. We also present a real-time version of one of our static people detection systems.