Automatic Parameter Adaptation for Multi-object Tracking

Object tracking quality usually depends on video context (e.g. object occlusion level, object density). In order to decrease this dependency, this paper presents a learning approach to adapt the tracker parameters to the context variations. In an offline phase, satisfactory tracking parameters are learned for video context clusters. In the online control phase, once a context change is detected, the tracking parameters are tuned using the learned values. The experimental results show that the proposed approach outperforms the recent trackers in state of the art. This paper brings two contributions: (1) a classification method of video sequences to learn offline tracking parameters, (2) a new method to tune online tracking parameters using tracking context.Comment: International Conference on Computer Vision Systems (ICVS) (2013

A Cognitive Vision Approach to Image Segmentation

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A Learning Approach for Adaptive Image Segmentation

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Online Tracking Parameter Adaptation based on Evaluation

Parameter tuning is a common issue for many tracking algorithms. In order to solve this problem, this paper proposes an online parameter tuning to adapt a tracking algorithm to various scene contexts. In an offline training phase, this approach learns how to tune the tracker parameters to cope with different contexts. In the online control phase, once the tracking quality is evaluated as not good enough, the proposed approach computes the current context and tunes the tracking parameters using the learned values. The experimental results show that the proposed approach improves the performance of the tracking algorithm and outperforms recent state of the art trackers. This paper brings two contributions: (1) an online tracking evaluation, and (2) a method to adapt online tracking parameters to scene contexts.Comment: IEEE International Conference on Advanced Video and Signal-based Surveillance (2013

Comment vieillir chez soi

National audienceLe vieillissement de la population se traduit par une augmentation du nombre de personnes à autonomie réduite mais aussi par une croissance des victimes de la maladie d'Alzheimer. Comment améliorer leur prise en charge

Automatic morphological description of galaxies and classification by an expert system

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Semantic Activity Recognition

International audienceExtracting automatically the semantics from visual data is a real challenge. We describe in this paper how recent work in cognitive vision leads to significative results in activity recognition for visualsurveillance and video monitoring. In particular we present work performed in the domain of video understanding in our PULSAR team at INRIA in Sophia Antipolis. Our main objective is to analyse in real-time video streams captured by static video cameras and to recognize their semantic content. We present a cognitive vision approach mixing 4D computer vision techniques and activity recognition based on a priori knowledge. Applications in visualsurveillance and healthcare monitoring are shown. We conclude by current issues in cognitive vision for activity recognition

An APRIORI-based Method for Frequent Composite Event Discovery in Videos

We propose a method for discovery of composite events in videos. The algorithm processes a set of primitive events such as simple spatial relations between objects obtained from a tracking system and outputs frequent event patterns which can be interpreted as frequent composite events. We use the APRIORI algorithm from the field of data mining for efficient detection of frequent patterns. We adapt this algorithm to handle temporal uncertainty in the data without losing its computational effectiveness. It is formulated as a generic framework in which the context knowledge is clearly separated from the method in form of a similarity measure for comparison between two video activities and a library of primitive events serving as a basis for the composite events

Robust Mobile Object Tracking Based on Multiple Feature Similarity and Trajectory Filtering

This paper presents a new algorithm to track mobile objects in different scene conditions. The main idea of the proposed tracker includes estimation, multi-features similarity measures and trajectory filtering. A feature set (distance, area, shape ratio, color histogram) is defined for each tracked object to search for the best matching object. Its best matching object and its state estimated by the Kalman filter are combined to update position and size of the tracked object. However, the mobile object trajectories are usually fragmented because of occlusions and misdetections. Therefore, we also propose a trajectory filtering, named global tracker, aims at removing the noisy trajectories and fusing the fragmented trajectories belonging to a same mobile object. The method has been tested with five videos of different scene conditions. Three of them are provided by the ETISEO benchmarking project (http://www-sop.inria.fr/orion/ETISEO) in which the proposed tracker performance has been compared with other seven tracking algorithms. The advantages of our approach over the existing state of the art ones are: (i) no prior knowledge information is required (e.g. no calibration and no contextual models are needed), (ii) the tracker is more reliable by combining multiple feature similarities, (iii) the tracker can perform in different scene conditions: single/several mobile objects, weak/strong illumination, indoor/outdoor scenes, (iv) a trajectory filtering is defined and applied to improve the tracker performance, (v) the tracker performance outperforms many algorithms of the state of the art

Special issue on Intelligent Vision Systems

International audienceThe explosive growth in the number of video cameras and the increasing computing power of computers extend the potential number of applications of vision algorithms. It brings also new challenges to the vision community. In particular the design of intelligent and robust vision systems becomes a rising topic for both researchers and developers from academic fields and industriesworldwide. With this special issue we intend to cover all aspects of Intelligent Vision Systems. By this we mean vision systems that interact with or respond to their environment in a dynamic and adaptive manner, with an emphasis put on integrated systems that are robust enough to be deployed in largely unconstrained environments