Yong Wang

Bio: Yong Wang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Systems architecture & Swarm intelligence. The author has an hindex of 1, co-authored 3 publications receiving 5 citations.

Challenge of multi-camera tracking

Abstract: Multi-camera tracking is quite different from single camera tracking in mathematical principles and application scenarios, and it faces new technology and system architecture challenges. The existing theories and algorithms used in object matching, cameras calibration and topology estimation, and information fusion have been reviewed and show that the integrated application of multi techniques and multi theories is the key to solve the technology challenges. The distributed architectures of multi-camera tracking system based on camera processor and based on object agent have been compared and show that improving the computation ability of cameras and reducing the functions of control center is the key to solve the architecture challenges.

Challenge of Multi-Camera Tracking

Abstract: Multi-camera tracking is quite different from single camera tracking, and it faces new technology and system architecture challenges. By analyzing the corresponding characteristics and disadvantages of the existing algorithms, problems in multi-camera tracking are summarized and some new directions for future work are also generalized.

Input Plane Preprocessing of Joint Transform Correlator Based on Swarm Intelligence

Abstract: Input plane image preprocessing can improve the performance of Joint Transform Correlator(JTC). Swarm Intelligence based image processing method is consistent with the parallel characteristic of optical signal processing method. In this paper,A Swarm Intelligence based input plane preprocessing method is proposed. Some other methods are also used to perform the task, such as grayscale transform, Sobel operator and mathematical morphology. The experimental results show that the performance of the proposed Swarm Intelligence preprocessing based JTC is better than that of the JTC based on other preprocessing methods.

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Abstract: Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behavior modelling, prediction and interaction control.

Literature Survey on Multi-Camera System and Its Application

Abstract: A multi-camera system combines features from different cameras to exploit a scene of an event to increase the output image quality. The combination of two or more cameras requires prior settings in terms of calibration and architecture. Therefore, this paper surveys the available literature in terms of multi-camera systems’ physical arrangements, calibrations, algorithms, and their advantages and disadvantages. We also survey the recent developments and advancements in four areas of multi-camera system applications, which are surveillance, sports, education, and mobile phones. In the surveillance system, the combination of multiple heterogeneous cameras and the discovery of Pan-Tilt-Zoom (PTZ) and smart cameras have brought tremendous achievements in the area of multi-camera control and coordination. Different approaches have been proposed to facilitate effective collaboration and monitoring among the camera network. Furthermore, the application of multi-cameras in sports has made the games more interesting in the aspect of analyses and transparency. The application of the multi-camera system in education has taken education beyond the four walls of the class. The method of teaching, student attendance enrollment, determination of students’ attention, teacher and student assessment can now be determined with ease, and all forms of proxy and manipulation in education can be reduced by using a multi-camera system. Besides, the number of cameras featuring on smartphones is gaining noticeable recognition. However, most of these cameras serve different purposes, from zooming, telephoto, and wider Field of View (FOV). Therefore, future smartphones should be expecting more cameras or the development would be in a different direction.

Orientation and scale invariant binary descriptor based on Haar wavelet

Abstract: In this paper, an orientation and scale invariant binary descriptor is proposed, which can be used in key-points matching systems. Conventionally, a binary descriptor is generated by comparing the intensities of pixels directly, such as those in Binary Robust Independent Elementary Features (BRIEF) and Oriented FAST and Rotated BRIEF (ORB). However, comparing intensities of pixels may lose the texture information in the region of interest, and lead to a high false match rate in a practical application. In our proposed method, the region of interest is segmented into grid cells and then the binary Haar wavelet responses are computed to store the texture information of the patch. Concretely, the texture information in each cell is expressed by the horizontal and vertical gradients and the polarity of intensity changes which are indicated by four components of Haar wavelet response. The binary descriptor is generated by comparing the Haar wavelet response in each pair of grid cells. Furthermore, to be scale and orientation invariant, the patch of key-points is rotated to the primary direction of the centroid vector in the image pyramid. Extensive experimental results show that our descriptor significantly outperforms other five state-of-the-art binary descriptors in key-point matching systems. The average percentage of correct matches of our method is 32.79% higher than that for FREAK and 5.31% higher than that for LDB.

Descriptor Extraction and Distance Metric Learning for a Robust Person Re-Identification System

Abstract: Person re-identification is a topic under computer vision and video surveillance and came to light as a solution to a multi-camera visual tracking problem of finding object correspondence between cameras. Unfortunately, developing a person re-identification system is non-trivial as it involves many challenges. The study was aimed to develop a re-identification system that deals with common re-identification problems, as well as real-world problems. With all the challenges presented as majority of existing works have only now started to consider real-world problems. The system made use of different color and texture features and also made use of a distance metric learning algorithm called Keep it Simple and Straightforward metric or KISSME. The performance of the re-identification system was tested by applying it on chosen datasets such as the VIPeR dataset, CUHK01, CUHK02, CUHK03, as well as the Market-1501 dataset and then examining the accuracy scores from metrics such as the cumulative matching characteristics curve. The results that were acquired from the VIPeR, CUHK01, and CUHK02 datasets show that the system is comparable to the chosen baseline system with differences of only about 1-4% differences while the system outperforms the baseline on the Market-1501 dataset. The re-identification system was also found to be more scalable than the chosen baseline system.

Moving object tracking apparatus, moving object tracking method, and computer-readable medium

Abstract: According to an arrangement, a moving object tracking apparatus (20; 20-2) includes an acquiring unit (211), an associating unit (212; 212-2), and an output control unit (213; 213-2). The acquiring unit (211) is configured to acquire a plurality of pieces of moving object information representing a moving object included in a photographed image. The associating unit (212; 212-2) is configured to execute an associating process for associating a plurality of pieces of the moving object information similar to each other as the moving object information of the same moving object for three or more pieces of the moving object information. The output control unit (213; 213-2) is configured to output the associated moving object information.