Section: New Results

Video Understanding for Group Behavior Analysis

Participants : Carolina Garate, François Brémond.

keywords: Computer vision, group tracking, scene understanding, group behavior recognition, video surveillance, event detection.

The main work in this PhD thesis concerns the recognition of the behaviors of a group of people (2-5 persons) involved in a scene depicted by a video sequence.

Our goal focuses on the automatic recognition of behavior patterns in video sequence for groups of people (2-5 persons). We want to build a real time system able to recognize various group scenarios.

The approach includes different tasks to achieve the final recognition. The first one consists in tracking groups of moving regions detected in the video sequence acquired by the cameras. The second task attempts to classify these moving regions into people classes. Finally, the last task recognizes group scenarios using a priori knowledge containing scenario models predefined by experts and also 3D geometric and semantic information of the observed environment.

Our approach considers a chain process consisting of 5 consecutive steps for video processing. The steps are : 1) segmentation, 2) physical object detection, 3) physical objects tracking, 4) group tracking and 5) group behavior recognition. Our research focuses on the last two phases.

First, group scenarios have been defined (and then recognized) using the general scenario description language. Second, the likelihood of the group scenario recognition has been quantified. Third, machine learning techniques have been investigated to learn and recognize these scenarios.

We have processed the data set from 1 month video surveillance camera in the Torino subway and the Minds eye data set. Recognizing several and different events such as: walking groups, standing still groups, running groups, calm groups (i.e. having a bounding box with stable size), active groups (i.e. with bounding box's size variations, meaning that group members move a lot).