Biologically inspired composite image sensor for deep field target tracking

The use of nonuniform image sensors in mobile based computer vision applications can be an effective solution when computational burden is problematic. Nonuniform image sensors are still in their infancy and as such have not been fully investigated for their unique qualities nor have they been extensively applied in practice. In this dissertation a system has been developed that can perform vision tasks in both the far field and the near field. In order to accomplish this, a new and novel image sensor system has been developed. Inspired by the biological aspects of the visual systems found in both falcons and primates, a composite multi-camera sensor was constructed. The sensor provides for expandable visual range, excellent depth of field, and produces a single compact output image based on the log-polar retinal-cortical mapping that occurs in primates. This mapping provides for scale and rotational tolerant processing which, in turn, supports the mitigation of perspective distortion found in strict Cartesian based sensor systems. Furthermore, the scale-tolerant representation of objects moving on trajectories parallel to the sensor\u27s optical axis allows for fast acquisition and tracking of objects moving at high rates of speed. In order to investigate how effective this combination would be for object detection and tracking at both near and far field, the system was tuned for the application of vehicle detection and tracking from a moving platform. Finally, it was shown that the capturing of license plate information in an autonomous fashion could easily be accomplished from the extraction of information contained in the mapped log-polar representation space. The novel composite log-polar deep-field image sensor opens new horizons for computer vision. This current work demonstrates features that can benefit applications beyond the high-speed vehicle tracking for drivers assistance and license plate capture. Some of the future applications envisioned include obstacle detection for high-speed trains, computer assisted aircraft landing, and computer assisted spacecraft docking

Object Re-Identification Based on Deep Learning

With the explosive growth of video data and the rapid development of computer vision technology, more and more relevant technologies are applied in our real life, one of which is object re-identification (Re-ID) technology. Object Re-ID is currently concentrated in the field of person Re-ID and vehicle Re-ID, which is mainly used to realize the cross-vision tracking of person/vehicle and trajectory prediction. This chapter combines theory and practice to explain why the deep network can re-identify the object. To introduce the main technical route of object Re-ID, the examples of person/vehicle Re-ID are given, and the improvement points of existing object Re-ID research are described separately

Unified Chinese License Plate Detection and Recognition with High Efficiency

Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP images as a supplement to the existing public benchmarks. The images are mainly captured with electronic monitoring systems with detailed annotations. To our knowledge, CRPD is the largest public multi-objective Chinese LP dataset with annotations of vertices. With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline. The network is end-to-end trainable with totally real-time inference efficiency (30 fps with 640p). The experiments on several public benchmarks demonstrate that our method has reached competitive performance. The code and dataset will be publicly available at https://github.com/yxgong0/CRPD

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions