FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation
Philip Lenz,Andreas Geiger,Raquel Urtasun +2 more
- pp 4364-4372
TLDR
In this article, a dynamic version of the successive shortest-path algorithm is introduced to solve the data association problem optimally while reusing computation, resulting in faster inference than standard solvers.Abstract:
One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrary length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.read more
Citations
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Journal ArticleDOI
Multiple object tracking: A literature review
TL;DR: This work provides a thorough review on the development of this problem in recent decades and inspects the recent advances in various aspects and proposes some interesting directions for future research.
Posted Content
Multiple Object Tracking: A Literature Review
TL;DR: In this article, a comprehensive and most recent review on the state-of-the-art multiple object tracking (MOT) methods is presented, in which existing approaches are divided into different groups and each group is discussed in detail for the principles, advances and drawbacks.
Proceedings ArticleDOI
Deep Network Flow for Multi-object Tracking
TL;DR: In this article, the optimality of a smoothed network flow problem is expressed as a differentiable function of the pairwise association costs, which can then be learned from data.
Proceedings ArticleDOI
FAMNet: Joint Learning of Feature, Affinity and Multi-Dimensional Assignment for Online Multiple Object Tracking
Peng Chu,Haibin Ling +1 more
TL;DR: FAMNet as discussed by the authors proposes an end-to-end model, named FAMNet, where feature extraction, affinity estimation and multi-dimensional assignment are refined in a single network, which can be optimized jointly to learn the discriminative features and higher-order affinity model for robust MOT, which is supervised by the loss directly from the assignment ground truth.
Journal ArticleDOI
Multi-Target Tracking by Discrete-Continuous Energy Minimization
TL;DR: This work presents a multi-target tracking approach that explicitly models both tasks as minimization of a unified discrete-continuous energy function and introduces pairwise label costs to describe mutual interactions between targets in order to avoid collisions.
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