Dennis Mitzel

Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras

We present a real-time RGB-D based multiperson detection and tracking system suitable for mobile robots and head-worn cameras. Our approach combines RGB-D visual odometry estimation, region-of-interest processing, ground plane estimation, pedestrian detection, and multi-hypothesis tracking components into a robust vision system that runs at more than 20fps on a laptop. As object detection is the most expensive component in any such integration, we invest significant effort into taking maximum advantage of the available depth information. In particular, we propose to use two different detectors for different distance ranges. For the close range (up to 5-7m), we present an extremely fast depth-based upper-body detector that allows video-rate system performance on a single CPU core when applied to Kinect sensors. In order to cover also farther distance ranges, we optionally add an appearance-based full-body HOG detector (running on the GPU) that exploits scene geometry to restrict the search space. Our approach can work with both Kinect RGB-D input for indoor settings and with stereo depth input for outdoor scenarios. We quantitatively evaluate our approach on challenging indoor and outdoor sequences and show state-of-the-art performance in a large variety of settings. Our code is publicly available.

Multi-person tracking with sparse detection and continuous segmentation

This paper presents an integrated framework for mobile street-level tracking of multiple persons. In contrast to classic tracking-by-detection approaches, our framework employs an efficient level-set tracker in order to follow individual pedestrians over time. This low-level tracker is initialized and periodically updated by a pedestrian detector and is kept robust through a series of consistency checks. In order to cope with drift and to bridge occlusions, the resulting tracklet outputs are fed to a high-level multi-hypothesis tracker, which performs longer-term data association. This design has the advantage of simplifying short-term data association, resulting in higher-quality tracks that can be maintained even in situations where the pedestrian detector does no longer yield good detections. In addition, it requires the pedestrian detector to be active only part of the time, resulting in computational savings. We quantitatively evaluate our approach on several challenging sequences and show that it achieves state-of-the-art performance.

Taking mobile multi-object tracking to the next level: people, unknown objects, and carried items

In this paper, we aim to take mobile multi-object tracking to the next level. Current approaches work in a tracking-by-detection framework, which limits them to object categories for which pre-trained detector models are available. In contrast, we propose a novel tracking-before-detection approach that can track both known and unknown object categories in very challenging street scenes. Our approach relies on noisy stereo depth data in order to segment and track objects in 3D. At its core is a novel, compact 3D representation that allows us to robustly track a large variety of objects, while building up models of their 3D shape online. In addition to improving tracking performance, this representation allows us to detect anomalous shapes, such as carried items on a persons body. We evaluate our approach on several challenging video sequences of busy pedestrian zones and show that it outperforms state-of-the-art approaches.

Real-time multi-person tracking with detector assisted structure propagation

Classical tracking-by-detection approaches require a robust object detector that needs to be executed in each frame. However the detector is typically the most computationally expensive component, especially if more than one object class needs to be detected. In this paper we investigate how the usage of the object detector can be reduced by using stereo range data for following detected objects over time. To this end we propose a hybrid tracking framework consisting of a stereo based ICP (Iterative Closest Point) tracker and a high-level multi-hypothesis tracker. Initiated by a detector response, the ICP tracker follows individual pedestrians over time using just the raw depth information. Its output is then fed into the high-level tracker that is responsible for solving long-term data association and occlusion handling. In addition, we propose to constrain the detector to run only on some small regions of interest (ROIs) that are extracted from a 3D depth based occupancy map of the scene. The ROIs are tracked over time and only newly appearing ROIs are evaluated by the detector. We present experiments on real stereo sequences recorded from a moving camera setup in urban scenarios and show that our proposed approach achieves state of the art performance.

Real-Time Multi-Person Tracking with Time-Constrained Detection

This paper presents a robust real-time multi-person tracking framework for busy street scenes. Tracking-by-detection approaches have recently been successfully applied to this task. However, their run-time is still limited by the computationally expensive object detection component. In this paper, we therefore consider the problem of making best use of an object detector with a fixed and very small time budget. The question we ask is: given a fixed time budget that allows for detector-based verification of k small regions-of-interest (ROIs) in the image, what are the best regions to attend to in order to obtain stable tracking performance? We address this problem by applying a statistical Poisson process model in order to rate the urgency by which individual ROIs should be attended to. These ROIs are initially extracted from a 3D depth-based occupancy map of the scene and are then tracked over time. This allows us to balance the system resources in order to satisfy the twin goals of detecting newly appearing objects, while maintaining the quality of existing object trajectories.

Tracking People and Their Objects

Current pedestrian tracking approaches ignore important aspects of human behavior. Humans are not moving independently, but they closely interact with their environment, which includes not only other persons, but also different scene objects. Typical everyday scenarios include people moving in groups, pushing child strollers, or pulling luggage. In this paper, we propose a probabilistic approach for classifying such person-object interactions, associating objects to persons, and predicting how the interaction will most likely continue. Our approach relies on stereo depth information in order to track all scene objects in 3D, while simultaneously building up their 3D shape models. These models and their relative spatial arrangement are then fed into a probabilistic graphical model which jointly infers pairwise interactions and object classes. The inferred interactions can then be used to support tracking by recovering lost object tracks. We evaluate our approach on a novel dataset containing more than 15,000 frames of person-object interactions in 325 video sequences and demonstrate good performance in challenging real-world scenarios.

Geometrically constrained level set tracking for automotive applications

We propose a new approach for integrating geometric scene knowledge into a level-set tracking framework. Our approach is based on a novel constrained-homography transformation model that restricts the deformation space to physically plausible rigid motion on the ground plane. This model is especially suitable for tracking vehicles in automotive scenarios. Apart from reducing the number of parameters in the estimation, the 3D transformation model allows us to obtain additional information about the tracked objects and to recover their detailed 3D motion and orientation at every time step. We demonstrate how this information can be used to improve a Kalman filter estimate of the tracked vehicle dynamics in a higher-level tracker, leading to more accurate object trajectories. We show the feasibility of this approach for an application of tracking cars in an inner-city scenario.

A fixed-dimensional 3D shape representation for matching partially observed objects in street scenes

In this paper, we present an object-centric, fixed-dimensional 3D shape representation for robust matching of partially observed object shapes, which is an important component for object categorization from 3D data. A main problem when working with RGB-D data from stereo, Kinect, or laser sensors is that the 3D information is typically quite noisy. For that reason, we accumulate shape information over time and register it in a common reference frame. Matching the resulting shapes requires a strategy for dealing with partial observations. We therefore investigate several distance functions and kernels that implement different such strategies and compare their matching performance in quantitative experiments. We show that the resulting representation achieves good results for a large variety of vision tasks, such as multi-class classification, person orientation estimation, and articulated body pose estimation, where robust 3D shape matching is essential.

Real time vision based multi-person tracking for mobile robotics and intelligent vehicles

In this paper, we present a real-time vision-based multi-person tracking system working in crowded urban environments. Our approach combines stereo visual odometry estimation, HOG pedestrian detection, and multi-hypothesis tracking-by-detection to a robust tracking framework that runs on a single laptop with a CUDA-enabled graphics card. Through shifting the expensive computations to the GPU and making extensive use of scene geometry constraints we could build up a mobile system that runs with 10Hz. We experimentally demonstrate on several challenging sequences that our approach achieves competitive tracking performance.