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Online Multi-target Tracking by Large Margin Structured Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

We present an online data association algorithm for multi-object tracking using structured prediction. This problem is formulated as a bipartite matching and solved by a generalized classification, specifically, Structural Support Vector Machines (S-SVM). Our structural classifier is trained based on matching results given the similarities between all pairs of objects identified in two consecutive frames, where the similarity can be defined by various features such as appearance, location, motion, etc. With an appropriate joint feature map and loss function in the S-SVM, finding the most violated constraint in training and predicting structured labels in testing are modeled by the simple and efficient Kuhn-Munkres (Hungarian) algorithm in a bipartite graph. The proposed structural classifier can be generalized effectively for many sequences without re-training. Our algorithm also provides a method to handle entering/leaving objects, short-term occlusions, and misdetections by introducing virtual agents—additional nodes in a bipartite graph. We tested our algorithm on multiple datasets and obtained comparable results to the state-of-the-art methods with great efficiency and simplicity.

Keywords

Feature Vector Bipartite Graph Local Binary Pattern Consecutive Frame Data Association 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPOSTECHKorea

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