Survey on video anomaly detection in dynamic scenes with moving cameras

The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie

Efficient Recognition of Planar Objects Based on Hashing of Keypoints — An Approach Towards Making the Physical World Clickable

This paper presents a method of planar object recognition for aiming at accessing information about objects by taking pictures of them. For this purpose efficiency of processing is the central issue because current state-of-the-art technologies with tree structures do not necessarily work well with a large amount of data represented as high dimensional vectors. To solve this problem, we employ hashing of keypoints extracted from images of objects. With the help of hash keys obtained as integers converted from the real valued vectors, keypoints are stored with object IDs and retrieved with no search process. Voting for object IDs is employed to determine a recognized object as the one with the largest vote. Experimental results show that the proposed method is at least 400 times faster than a brute-force method while 90 % of objects were correctly recognized. 1