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In our previous work, we reported a system that monitors an intersection using a network of horizontal laser scanners. This paper focuses on an algorithm for moving-object detection and tracking, given a sequence of distributed laser scan data of an intersection. The goal is to detect each moving object that enters the intersection; estimate state parameters such as size; and track its location, speed, and direction while it passes through the intersection. This work is unique, to the best of the authors' knowledge, in that the data is novel, which provides new possibilities but with great challenges; the algorithm is the first proposal that uses such data in detecting and tracking all moving objects that pass through a large crowded intersection with focus on achieving robustness to partial observations, some of which result from occlusions, and on performing correct data associations in crowded situations. Promising results are demonstrated using experimental data from real intersections, whereby, for 1063 objects moving through an intersection over 20 min, 988 are perfectly tracked from entrance to exit with an excellent tracking ratio of 92.9%. System advantages, limitations, and future work are discussed.

In this research, a novel system for monitoring an intersection using a network of single-row laser range scanners (subsequently abbreviated as "laser scanner") and video cameras is proposed. Laser scanners are set on the road side to profile an intersection horizontally from different viewpoints. The contour points of moving objects are captured at a certain horizontal plane with a high scanning rate (e.g., 37 Hz). A laser-based processing algorithm is developed, thus the moving objects entered the intersection are detected and tracked to estimate their state parameters, such as: location, speed, and direction at each time instance. In addition, laser data and processing results are forwarded to an associated video camera, so that a visualization as well as fusion-based processing can be achieved. An experiment in central Beijing is presented, demonstrating that a large quantity of physical dimension and detailed traffic data can be obtained through such a system.

Inspite extensive research on visual tracking of multiple people in computer vision area, the robustness and usability of visual trackers are still discouraging. Recently, a few laser-based detection and tracking methods have been developed in robotics area. However, poor features provided by laser data make the tracker fail in many situations. In this paper, we present a novel method that aims at reliably detecting and tracking multiple people in an open area. Multiple laser scanners and one camera are used as input sensors. In detection stage, laser-based detection algorithm captures newly appeared people and initializes the mean-shift-based visual tracker. In tracking stage, laser-based feet trajectory tracking result and visual body region tracking result are combined with a decision-level Bayesian fusion method. The experimental results demonstrate reliable and real-time performance of the method.

Laser-based people tracking systems have been developed for mobile robotic, and intelligent surveillance areas. Existing systems rely on laser point clustering method to extract object locations. However, for dense crowd tracking, laser points of different objects are often interlaced and undistinguishable due to measurement noise and they can not provide reliable features. It causes current systems quite fragile and unreliable. This paper presents a novel and robust laser-based dense crowd tracking method. Firstly, we introduce a stable feature extraction method based on accumulated distribution of successive laser frames. With this method, the noise that generates split and merged measurements is smoothed away and the pattern of rhythmic swing legs is utilized to extract each leg of persons. And then, a region coherency property is introduced to construct an efficient measurement likelihood model. The final tracker is based on the combination of independent Kalman filter and Rao-Blackwellized Monte Carlo data association filter (RBMC-DAF). In real experiments, we obtain raw data from multiple registered laser scanners, which measure two legs for each people on the height of 16cm from horizontal ground. Evaluation with real data shows that the proposed method is robust and effective. It achieves a significant improvement compared with existing laser-based trackers. In addition, the proposed method is much faster than previous works, and can overcome tracking errors resulted from mixed data of two closely situated persons.

Working environments of modern robots have changed to unstructured, dynamic and outdoor scenes. There emerged several new challenges along with these changes, mainly in perception of both static and dynamic objects of the scenes. To tackle these new challenges, this research focused on study of advanced perception systems that can simultaneously model static scenes and track dynamic objects. Our research has three features. Multi-view and multi-type sensors, together with machine learning based algorithms, are utilized to obtain robust and reliable mapping/ tracking results. In addition, a car-based mobile perception system is developed for exploring large sites. Finally, to improve robustness of the multi-view and mobile perception system, some new camera calibration methods are proposed. This paper presents an overview of our recent study on above mentioned ideas and technologies. Specifically we will focus on multi-sensor based multiple target tracking, simultaneous 3D mapping and target tracking in a mobile platform, and camera calibration.

Motivated by two potential applications, i.e. enhancing driving safety and traffic data collection, a system has been developed using a single-layer horizontal laser scanner as the major sensor for both localization and perception of the surroundings in a large dynamic urban environment. This research focuses on a classification method, that given a stream of laser measurements, classify the moving object into either a person, a group of people, a bicycle or a car. In this research, a number of features are defined after examining the property of data appearance. A classification method is proposed after examining the likelihood measures between each pair of feature and class. Experimental results are presented, demonstrating that the algorithm has efficiency with respect to both driving safety and traffic data collection in highly dynamic environment.

This chapter presents an approach to detect and track multiple people ro bustly in real time using laser scan frames. The detection and tracking of people in real time is a problem that arises in a variety of different contexts. Examples in clude intelligent surveillance for security purposes, scene analysis for service robot, and crowd behavior analysis for human behavior study. Over the last several years, an increasing number of laser-based people-tracking systems have been developed in both mobile robotics platforms and fixed platforms using one or multiple laser scanners. It has been proved that processing on laser scanner data makes the tracker much faster and more robust than a vision-only based one in complex situations. In this chapter, we present a novel robust tracker to detect and track multiple people in a crowded and open area in real time. First, raw data are obtained that measures two legs for each people at a height of 16 cm from horizontal ground with multiple registered laser scanners. A stable feature is extracted using accumulated distribu tion of successive laser frames. In this way, the noise that generates split and merged measurements is smoothed well, and the pattern of rhythmic swinging legs is uti lized to extract each leg. Second, a probabilistic tracking model is presented, and then a sequential inference process using a Bayesian rule is described. A sequential inference process is difficult to compute analytically, so two strategies are presented to simplify the computation. In the case of independent tracking, the Kalman fil ter is used with a more efficient measurement likelihood model based on a region coherency property. Finally, to deal with trajectory fragments we present a concise approach to fuse just a little visual information from synchronized video camera to laser data. Evaluation with real data shows that the proposed method is robust and effective. It achieves a significant improvement compared with existing laser-based trackers.

In this research, a novel system for monitoring an intersection using a network of single-row laser range scanners (subsequently abbreviated as "laser scanner") is proposed. Laser scanners are set on the road side to profile an intersection horizontally from different viewpoints. This is done so that cross sections of the intersection are captured at a high scanning rate (e.g., 37 Hz) and to contain the contour points of the moving objects entering the intersection. Different laser scanners data are integrated into a common spatial-temporal coordinate system and processed. Thus, the moving objects inside the intersection are detected and tracked to estimate their state parameters, such as: location, speed, and direction at each time instance. An experiment was conducted in central Beijing, where six laser scanners were used to cover a three-way intersection. A digital copy of the dynamic intersection was measured, and, through data processing, a large quantity of physical dimension traffic data was obtained.

This research is motivated by two potential applications - enhancing driving safety and collecting traffic data in a large dynamic urban environment. A laser scanner based approach is proposed, in which SLAM (simultaneous localization and mapping) is developed with moving object detection and tracking using a laser scanner for perception, using GPS to achieve global accuracy, and using yaw rate and wheel speed to diagnose pose errors. Experiments are conducted to collect data along a course (4.5 km) with a test-bed vehicle run in a highly dynamic environment. The algorithms are examined, possibilities with respect to the two potential applications are demonstrated, and future works are discussed.

In this research, we propose a method of SLAM in a dynamic large outdoor environment using a laser scanner. Focus are cast on solving two major problems: 1) achieving global accuracy especially in non-cyclical environment, 2) tackling a mixture of data from both dynamic and static objects. Algorithms are developed, where GPS data and control inputs are used to diagnose pose error and guide to achieve a global accuracy; Classification of laser points and objects are conducted not in an independent module but across the processing in a framework of SLAM with moving object detection and tracking. Experiments are conducted using the data from two test-bed vehicles, and performance of the algorithms are demonstrated.

Successful multi-target tracking requires locating the targets and labeling their identities. For the laser based tracking system, the latter becomes significantly more challenging when the targets frequently interact with each other. This paper presents a novel on-line supervised learning based method for tracking interacting targets with laser scanner. When the targets do not interact with each other, we collect samples and train a classifier for each target. When the targets are in close proximity, we use these classifiers to assist in tracking. Different evaluations demonstrate that this method has a better tracking performance than previous methods when interactions occur, and can maintain correct tracking under various complex tracking situations.

Laser based people tracking systems have been developed for mobile robotics and intelligent surveillance areas. Existing systems rely on simple laser point clustering methods to extract object locations. However, when dealing with multiple interacting people, laser points of different persons are often interlaced and undistinguishable due to measurement noise and they can not provide reliable features. It causes current systems quite fragile and unreliable. In this paper, we try to explore potentials from multi-level observations including weakly detected features, stably extracted features and foreground points. For inference, detection incorporated joint particle filter is used. And stably extracted features are utilized to properly estimate parameters of dynamic model for each target. In real experiments, we obtain raw data from multiple registered laser scanners, which measure two legs for each people. Evaluations with real data show that the proposed method is more robust and effective than existing approaches.

In this paper, we present evaluation of the performance of a laser-based multi-people detection and tracking system. We propose metrics for frame-based as well as object- based evaluation paradigms considering complexity in multi- target tracking. In frame-based evaluation, accuracy of people localization is tested without considering people identity. In object-based evaluation, identity is included, so that the cases of broken/merged/crossed trackers are evaluated. Finally, we applied these metrics to the tracking results on real data.