Category: Object tracking

An Object Tracking in Particle Filtering and Data Association Framework, Using SIFT Features

An article published in ICDP 2011.

Authors: M. Souded, L. Giulieri and F. Bremond

The authors address the problematic of the multi-object tracking in video surveillance context with single static cameras. They propose a novel approach for multi-object tracking in a particle filtering and data association framework allowing real-time tracking and dealing with the most important challenges (1) selecting and tracking real object of interest in noisy environments (2) managing situations of occlusion. In this study they consider tracker inputs from motion detection approach (classically based on background subtraction and clustering). Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. The article presents SIFT features tracking into a particle filtering and data association. The performance of the proposed algorithm is evaluated on sequences from ETISEO, CAVIAR, PETS2001 and VS-PETS2003 datasets in order to show the improvements upon state-of-the-art methods.

Diagram of the proposed object tracking framework



Full version of the article can be downloaded at here.

Object tracking in SUP

Involved people: Duc Phu CHAU, Francois BREMOND and Monique THONNAT

SUP (Scene Unsderstanding Platform, developped by Stars team) provides an object apperance-based tracking algorithm. This tracker includes two main plugins: ParametrableF2Ftracking and LTT.

The objective of ParametrableF2Ftracking plugin is to establish object links with a sliding time window. For each detected object pair in a given temporal window of size T1, we compute the link score (i.e. instantaneous similarity). A temporary link is established between two objects when their link score is greater or equal to a predefined threshold. At the end of this stage, we obtain a weighted graph whose vertices are the detected objects in the considered temporal window and whose edges are the temporarily established links. Each edge is associated with a score representing the link score between its two vertices (see the below figure). For each detected object, we search its matched objects in a predefined radius and in a given temporal window to establish possible links so that even when one mobile object cannot be detected in some frames, it still can find its successor objects.

The graph representing the established links of the detected objects in a temporal window of size T1 frames.

The goal of the LTT plugin is to determine the trajectories of the mobile objects. For each object ot detected at instant t (called son object), we consider all its matched objects (i.e. objects with temporarily established links) in previous frames (called father objects) that do not have yet official links (i.e. trajectories) to any objects detected at the following frames. For such an object pair, we define a global score in function of link score (i.e. instantenous score) and  long-term score.

The father object having the highest global similarity is considered as a temporary father of object ot. After considering all objects at instant t, if more than one object get a same object as a temporary father, the pair father-son corresponding to the highest global score value is kept, and the link between this pair is official (i.e. become officially a trajectory segment). A mobile object is no longer tracked if it cannot establish any official links in T1 consecutive frames.

A reference of this work can be found here.

Object tracking description

Involved people: Duc Phu CHAU, Julien BADIE and Malik SOUDED

The aim of an object tracking algorithm is to generate the trajectories of objects over time by locating their positions in every frame of video. An object tracker may also provide the complete region in the image that is occupied by the object at every time instant.

Duc Phu CHAU’s work focus on control methods to adapt the tracking process to the scene variations. Julien BADIE focus on the global tracking task whose objective is to correct the object trajectories. He is also studying the approaches to make the trackers smarter and more flexible. Malik SOUDED studies the mobile object tracking for a multi-camera system.