DeepAI AI Chat
Log In Sign Up

A multi-feature tracking algorithm enabling adaptation to context variations

by   Duc Phu Chau, et al.

We propose in this paper a tracking algorithm which is able to adapt itself to different scene contexts. A feature pool is used to compute the matching score between two detected objects. This feature pool includes 2D, 3D displacement distances, 2D sizes, color histogram, histogram of oriented gradient (HOG), color covariance and dominant color. An offline learning process is proposed to search for useful features and to estimate their weights for each context. In the online tracking process, a temporal window is defined to establish the links between the detected objects. This enables to find the object trajectories even if the objects are misdetected in some frames. A trajectory filter is proposed to remove noisy trajectories. Experimentation on different contexts is shown. The proposed tracker has been tested in videos belonging to three public datasets and to the Caretaker European project. The experimental results prove the effect of the proposed feature weight learning, and the robustness of the proposed tracker compared to some methods in the state of the art. The contributions of our approach over the state of the art trackers are: (i) a robust tracking algorithm based on a feature pool, (ii) a supervised learning scheme to learn feature weights for each context, (iii) a new method to quantify the reliability of HOG descriptor, (iv) a combination of color covariance and dominant color features with spatial pyramid distance to manage the case of object occlusion.


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

This paper presents a new algorithm to track mobile objects in different...

Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features

To overcome the problem of occlusion in visual tracking, this paper prop...

Automatic Parameter Adaptation for Multi-object Tracking

Object tracking quality usually depends on video context (e.g. object oc...

Online Tracking Parameter Adaptation based on Evaluation

Parameter tuning is a common issue for many tracking algorithms. In orde...

Real Time Detection Free Tracking of Multiple Objects Via Equilibrium Optimizer

Multiple objects tracking (MOT) is a difficult task, as it usually requi...

Repairing People Trajectories Based on Point Clustering

This paper presents a method for improving any object tracking algorithm...

Underwater Fish Tracking for Moving Cameras based on Deformable Multiple Kernels

Fishery surveys that call for the use of single or multiple underwater c...