Deep Network Flow for Multi-Object Tracking

Data association problems are an important component of many computer vision applications, with multi-object tracking being one of the most prominent examples. A typical approach to data association involves finding a graph matching or network flow that minimizes a sum of pairwise association costs, which are often either hand-crafted or learned as linear functions of fixed features. In this work, we demonstrate that it is possible to learn features for network-flow-based data association via backpropagation, by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs. We apply this approach to multi-object tracking with a network flow formulation. Our experiments demonstrate that we are able to successfully learn all cost functions for the association problem in an end-to-end fashion, which outperform hand-crafted costs in all settings. The integration and combination of various sources of inputs becomes easy and the cost functions can be learned entirely from data, alleviating tedious hand-designing of costs.Comment: Accepted to CVPR 201

Understanding Complex Human Behaviour in Images and Videos.

Understanding human motions and activities in images and videos is an important problem in many application domains, including surveillance, robotics, video indexing, and sports analysis. Although much progress has been made in classifying single person's activities in simple videos, little efforts have been made toward the interpretation of behaviors of multiple people in natural videos. In this thesis, I will present my research endeavor toward the understanding of behaviors of multiple people in natural images and videos. I identify four major challenges in this problem: i) identifying individual properties of people in videos, ii) modeling and recognizing the behavior of multiple people, iii) understanding human activities in multiple levels of resolutions and iv) learning characteristic patterns of interactions between people or people and surrounding environment. I discuss how we solve these challenging problems using various computer vision and machine learning technologies. I conclude with final remarks, observations, and possible future research directions.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99956/1/wgchoi_1.pd

Breaking the Chain: Liberation from the Temporal Markov Assumption for Tracking Human Poses

We present an approach to multi-target tracking that has expressive potential beyond the capabilities of chain-shaped hidden Markov models, yet has significantly reduced complexity. Our framework, which we call tracking-by-selection, is similar to tracking-by-detection in that it sepa-rates the tasks of detection and tracking, but it shifts tempo-ral reasoning from the tracking stage to the detection stage. The core feature of tracking-by-selection is that it reasons about path hypotheses that traverse the entire video instead of a chain of single-frame object hypotheses. A traditional chain-shaped tracking-by-detection model is only able to promote consistency between one frame and the next. In tracking-by-selection, path hypotheses exist across time, and encouraging long-term temporal consistency is as sim-ple as rewarding path hypotheses with consistent image fea-tures. One additional advantage of tracking-by-selection is that it results in a dramatically simplified model that can be solved exactly. We adapt an existing tracking-by-detection model to the tracking-by-selection framework, and show im-proved performance on a challenging dataset (introduced in [18]). 1

Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization

In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models. Our code and the synthetic dataset are available at https://github.com/YxZhxn/Ray3D .Comment: Accepted by CVPR 202

A Bayesian Approach to Tracking Learning Detection

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A unified framework for multi-target tracking and collective activity recognition

Abstract. We present a coherent, discriminative framework for simultaneously tracking multiple people and estimating their collective activities. Instead of treating the two problems separately, our model is grounded in the intuition that a strong correlation exists between a person’s motion, their activity, and the motion and activities of other nearby people. Instead of directly linking the solutions to these two problems, we introduce a hierarchy of activity types that creates a natural progression that leads from a specific person’s motion to the activity of the group as a whole. Our model is capable of jointly tracking multiple people, recognizing individual activities (atomic activities), the interactions between pairs of people (interaction activities), and finally the behavior of groups of people (collective activities). We also propose an algorithm for solving this otherwise intractable joint inference problem by combining belief propagation with a version of the branch and bound algorithm equipped with integer programming. Experimental results on challenging video datasets demonstrate our theoretical claims and indicate that our model achieves the best collective activity classification results to date