Alexandre Hervieu

A Statistical Video Content Recognition Method Using Invariant Features on Object Trajectories

This work is dedicated to a statistical trajectory-based approach addressing two issues related to dynamic video content understanding: recognition of events and detection of unexpected events. Appropriate local differential features combining curvature and motion magnitude are defined and robustly computed on the motion trajectories in the image sequence. These features are invariant to image translation, in-the-plane rotation and spatial scaling. The temporal causality of the features is then captured by hidden Markov models dedicated to trajectory description, whose states are properly quantized values. The similarity between trajectories is expressed by exploiting this quantization-based HMM framework. Moreover statistical techniques have been developed for parameter estimations. Evaluations of the method have been conducted on several data sets including real trajectories obtained from sport videos, especially Formula One and ski TV program. The novel method compares favorably with other methods including feature histogram comparisons, HMM/GMM modeling and SVM classification.

A HMM-Based Method for Recognizing Dynamic Video Contents from Trajectories

This paper describes an original method for classifying object motion trajectories in video sequences in order to recognize dynamic events. Similarities between trajectories are expressed from hidden Markov models representing each trajectory. We have favorably compared our method to several other ones, including histogram comparison, longest common subsequence distance and SVM classification. Trajectory features are computed from the curvature and velocity values at each point of the trajectory, so that they are invariant to translation, rotation and scale. We have evaluated our method on two sets of data, a first one composed of typical classes of synthetic trajectories (such as parabola or clothoid), and a second one formed with trajectories obtained by tracking cars in a Formula 1 race video.

Trajectory-based handball video understanding

This paper presents a content-based approach for understanding handball videos. Tracked players are characterized by their 2D trajectories in the court plane. The trajectories and their interactions are used to model visual semantics, i.e., the observed activity phases. To this end, hierarchical parallel semi-Markov models (HPaSMMs) are computed in order to take into account the temporal causalities of object motions. Players motions are characterized using velocity informations while their interactions are described by the distances between trajectories. We have evaluated our method on real video sequences, and have favorably compared with another method, i.e., hierarchical parallel hidden Markov models (HPaHMMs).

Minimum Pairwise Coverage Using Constraint Programming Techniques

This paper presented the global constraint pairwise that can be used to enforce the presence of a given pair within a set of test cases or configurations. It also introduced several optimizations for implementing a method that computes the minimum set of test cases that covers pairwise. In addition, the method, seen as a constraint optimization problem, provides a way to compromise between time and efficiency by allowing anytime interruption or time-contract execution. Our approach has been implemented and evaluated on several instances of a test configurations generation problems [5] where input variables are boolean only. We envision to address other instances of these problem where the variables take their values in larger finite domains.

Activity-based temporal segmentation for videos of interacting objects using invariant trajectory features

This paper presents a content-based approach for temporal segmentation of videos. Tracked objects are characterized by their 2D trajectories which are used in a meaningful way to model visual semantics, i.e., the observed single video object activities and their interactions. To this end, hierarchical semi-Markov chains (SMCs) are computed in order to take into account the temporal causalities of object motions. Object movements are characterized using local invariant features computed from the curvature and velocity values while interactions are represented by the temporal evolution of the distance between objects. We have evaluated our method on squash video sequences, and have favorably compared with other methods including hidden Markov models (HMMs).