Utilisation du contexte pour la détection et le suivi d'objets en vidéosurveillance

Matthieu Rogez 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Video-surveillance cameras are increasingly used in our environment. They are indeed present almost everywhere in the cities, supermarkets, airports, warehouses, etc. These cameras are used, among other things, in order to detect suspect behavior (an intrusion for instance) or to recognize a specific category of object or person (gender detection, license plates detection). Other applications also exist to count and/or track people in order to analyze their behavior. Due to the increasing number of cameras and the difficulty to achieve these tasks manually, several video analysis methods have been developed in order to address them automatically. In this thesis, we mainly focus on the detection and tracking of moving objects from a fixed camera. Unlike methods based solely on images captured by cameras, our approach integrates contextual pieces of information in order better interpret these images. Thus we propose to build a geometric and geolocalized model of the scene and the camera. This model is built directly from the pre-deployment studies of the cameras and uses the OpenStreetMap geographical database to build 3d models of buildings near the camera. We added to this model the ability to predict the position of the sun throughout the day and the resulting shadows in the scene. By predicting the shadows, and deleting them from the foreground mask, our method is able to improve the segmentation of pedestrians. Regarding the tracking of multiple mobile objects, we use the formalism of finite state machines to effectively model the states and possible transitions that an object is allowed to take. This allows us to tailor the processing of each object according to its state. We manage the inter-object occlusion using a collective tracking strategy. When taking part in an occlusion, objects are regrouped and tracked collectively. At the end of the occlusion, each object is re-identified and individual tracking resume. Our algorithm adapts to any type of ground-moving object (pedestrians, vehicles, etc.) and seamlessly integrates in the developed scene model. We have also developed several retro-actions taking advantage of the knowledge of tracked objects to improve the detections obtained with the background model. In particular, we tackle the issue of stationary objects often integrated erroneously in the background and we revisited the initial proposal regarding shadow removal. All proposed solutions have been implemented in the Foxstream products and are able to run in real-time.
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Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Monday, April 24, 2017 - 10:14:51 AM
Last modification on : Wednesday, November 20, 2019 - 2:55:54 AM


  • HAL Id : hal-01512618, version 1


Matthieu Rogez. Utilisation du contexte pour la détection et le suivi d'objets en vidéosurveillance. 2015. ⟨hal-01512618⟩



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