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The current highway abnormal event detection efficiency is low, the leakage rate is high, real-time poor, and so on, this paper proposed highway abnormal parking detection method based on space-time relationship. Firstly, it used the traffic flow frequency to quantify the split image and removed the smaller connectivity domain to extract the road surface information, then used the perspective geometry of the pinhole camera to normalize the same scale. It used YOLOv4 network to obtain the exact position of the vehicle target, and established the space-time information matrix. To detect abnormal region and the detection results can output by the update of the space-time matrix. The experimental results show that the accuracy of this method is 95% in long-distance scenes and 93% in crowded scenes. [ABSTRACT FROM AUTHOR]

We outline the history of photogrammetry from the aspects of, perspective geometry, camera, platform, measure methods and measure instruments, and summarize previous contributions to photogrammetry. A brief review of computer vision history is given. The tight connections between computer vision and photogrammetry are discussed in terms of geometric principles, and some differences in applications are also considered. From the aspect of semantics, we analyze the development of remote sensing and its relations to machine learning and computer vision, including their common approaches and different applications. The prevailing deep learning raised from connectionism is also reviewed and its successful applications in photogrammetry are analyzed. At last, we expect that the future development of photogrammetry will be more tightly cross-integrated with computer vision, machine learning and artificial intelligence. [ABSTRACT FROM AUTHOR]