Ryosuke Shibasaki

A novel system for tracking pedestrians using multiple single-row laser-range scanners

We propose a novel system for tracking pedestrians in a wide and open area, such as a shopping mall and exhibition hall, using a number of single-row laser-range scanners (LD-A), which have a profiling rate of 10 Hz and a scanning angle of 270/spl deg/. LD-As are set directly on the floor doing horizontal scanning at an elevation of about 20 cm above the ground, so that horizontal cross sections of the surroundings, containing moving feet of pedestrians as well as still objects, are obtained in a rectangular coordinate system of real dimension. The data of moving feet are extracted through background subtraction by the client computers that control each LD-A, and sent to a server computer, where they are spatially and temporally integrated into a global coordinate system. A simplified pedestrians walking model based on the typical appearance of moving feet is defined and a tracking method utilizing Kalman filter is developed to track pedestrians trajectories. The system is evaluated through both real experiment and computer simulation. A real experiment is conducted in an exhibition hall, where three LD-As are used covering an area of about 60/spl times/60 m/sup 2/. Changes in visitors flow during the whole exhibition day are analyzed, where in the peak hour, about 100 trajectories are extracted simultaneously. On the other hand, a computer simulation is conducted to quantitatively examine system performance with respect to different crowd density.

Laser-based detection and tracking of multiple people in crowds

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.

Tracking multiple people using laser and vision

We present a novel system that aims at reliably detecting and tracking multiple people in an open area. Multiple single-row laser scanners and one video camera are utilized. Feet trajectory tracking based on registration of distance information from multiple laser scanners and visual body region tracking based on color histogram are combined in a Bayesian formulation. Results from tests in a real environment are reported to demonstrate that the system can detect and track multiple people simultaneously with reliable and real-time performance.

Detection and tracking of multiple pedestrians by using laser range scanners

We propose a novel system for tracking multiple pedestrians in a crowded scene by exploiting single-row laser range scanners that measure distances of surrounding objects. A walking model is built to describe the periodicity of the movement of the feet in the spatial-temporal domain, and a mean-shift clustering technique in combination with spatial- temporal correlation analysis is applied to detect pedestrians. Based on the walking model, particle filter is employed to track multiple pedestrians. Compared with camera-based methods, our system provides a novel technique to track multiple pedestrians in a relatively large area. The experiments, in which over 300 pedestrians were tracked in 5 minutes, show the validity of the proposed system.

SLAM in a dynamic large outdoor environment using a laser scanner

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.

Laser-based Interacting People Tracking Using Multi-level Observations

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

Sensing an intersection using a network of laser scanners and video cameras

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.

Monitoring a populated environment using single-row laser range scanners from a mobile platform

In this research, we proposed a system of detecting and monitoring pedestrians motion trajectories at a populated and wide environment, such as exhibition hall, supermarket etc., using the horizontally profiling single-row laser range scanners on a mobile platform. A simplified walking model is defined to track the rhythmic swing feet at the ground level. Pedestrians are recognized by detecting the braided styles, which is a typical appearance that could discriminate the data of moving feet with other mobile and motionless objects. Two experiments are conducted. One is at the laboratory environment, the purpose of which is to examine the algorithm in details. Another is at an exhibition hall, a populated and wide environment, the purpose is to examine whether the system could be applied for practical needs. It is a big challenge, while the system did well. Pedestrians in the exhibition hall at the moment of measurement are detected. Their motion trajectories are extracted, and associated to the background map, which is made of the motionless objects, and covers the whole exhibition hall.

Moving object classification using horizontal laser scan data

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.

Tracking a variable number of pedestrians in crowded scenes by using laser range scanners

We propose a novel system for tracking a variable number of pedestrians in crowded scenes by exploiting laser range scanners. Based on the specific pattern generated by walking feet in the spatio-temporal domain, a walking model is constructed and applied to track pedestrians. To track interactive targets, an algorithm based on Interactive Multiple Particle Filters (IMPF) is proposed, whose computation load increases linearly with the number of targets. To handle a variable number of pedestrians, spatio-temporal correlation analysis in combination with a mean shift based clustering technique is proposed. Compared with camera-based surveillance methods, our system provides a novel technique for automatically tracking a large number of pedestrians in a relatively large area. The experiments, in which over 2600 pedestrians were tracked in 10 minutes at a 60 m times 20 m subway station, show the effectiveness of our proposed algorithm.