Learning a Robust Society of Tracking Parts

by   Elena Burceanu, et al.

Object tracking is an essential task in computer vision that has been studied since the early days of the field. Being able to follow objects that undergo different transformations in the video sequence, including changes in scale, illumination, shape and occlusions, makes the problem extremely difficult. One of the real challenges is to keep track of the changes in objects appearance and not drift towards the background clutter. Different from previous approaches, we obtain robustness against background with a tracker model that is composed of many different parts. They are classifiers that respond at different scales and locations. The tracker system functions as a society of parts, each having its own role and level of credibility. Reliable classifiers decide the tracker's next move, while newcomers are first monitored before gaining the necessary level of reliability to participate in the decision process. Some parts that loose their consistency are rejected, while others that show consistency for a sufficiently long time are promoted to permanent roles. The tracker system, as a whole, could also go through different phases, from the usual, normal functioning to states of weak agreement and even crisis. The tracker system has different governing rules in each state. What truly distinguishes our work from others is not necessarily the strength of individual tracking parts, but the way in which they work together and build a strong and robust organization. We also propose an efficient way to learn simultaneously many tracking parts, with a single closed-form formulation. We obtain a fast and robust tracker with state of the art performance on the challenging OTB50 dataset.


page 7

page 10


Learning a Robust Society of Tracking Parts using Co-occurrence Constraints

Object tracking is an essential problem in computer vision that has been...

Subpixel-Precise Tracking of Rigid Objects in Real-time

We present a novel object tracking scheme that can track rigid objects i...

Rt-Track: Robust Tricks for Multi-Pedestrian Tracking

Object tracking is divided into single-object tracking (SOT) and multi-o...

Improving Real-time Score Following in Opera by Combining Music with Lyrics Tracking

Fully automatic opera tracking is challenging because of the acoustic co...

Visual Tracking via Reliable Memories

In this paper, we propose a novel visual tracking framework that intelli...

Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects

Tracking objects in 3D space and predicting their 6DoF pose is an essent...

Long and Short Memory Balancing in Visual Co-Tracking using Q-Learning

Employing one or more additional classifiers to break the self-learning ...

Please sign up or login with your details

Forgot password? Click here to reset