Over the last decade, there has been a spike in criminal activity all around the globe. According to the Indian police department, vehicle theft is one of the least solved offenses, and almost 19% of all recorded cases are related to motor vehicle theft. To overcome these adversaries, we propose a real-time vehicle surveillance system, which detects and tracks the suspect vehicle using the CCTV video feed. The proposed system extracts various attributes of the vehicle such as Make, Model, Color, License plate number, and type of the license plate. Various image processing and deep learning algorithms are employed to meet the objectives of the proposed system. The extracted features can be used as evidence to report violations of law. Although the system uses more parameters, it is still able to make real time predictions with minimal latency and accuracy loss.
翻译:过去十年来,全球范围内的犯罪活动激增,据印度警察局称,车辆盗窃是解决最少的罪行之一,在所有记录在案的案件中,几乎有19%与机动车辆盗窃有关。为了克服这些对手,我们提议建立一个实时车辆监视系统,利用闭路电视视频传输探测和跟踪可疑车辆。拟议系统提取车辆的各种属性,如Make、Model、Color、车牌号以及车牌的类型。为了实现拟议系统的目标,采用了各种图像处理和深层学习算法。提取的特征可以用作报告违法行为的证据。虽然该系统使用更多的参数,但仍能够作出实时预测,同时尽可能减少延迟和准确的损失。