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

INVESTIGATING COLLABORATIVE EXPLAINABLE AI (CXAI)/SOCIAL FORUM AS AN EXPLAINABLE AI (XAI) METHOD IN AUTONOMOUS DRIVING (AD)

Explainable AI (XAI) systems primarily focus on algorithms, integrating additional information into AI decisions and classifications to enhance user or developer comprehension of the system\u27s behavior. These systems often incorporate untested concepts of explainability, lacking grounding in the cognitive and educational psychology literature (S. T. Mueller et al., 2021). Consequently, their effectiveness may be limited, as they may address problems that real users don\u27t encounter or provide information that users do not seek. In contrast, an alternative approach called Collaborative XAI (CXAI), as proposed by S. Mueller et al (2021), emphasizes generating explanations without relying solely on algorithms. CXAI centers on enabling users to ask questions and share explanations based on their knowledge and experience to facilitate others\u27 understanding of AI systems. Mamun, Hoffman, et al. (2021) developed a CXAI system akin to a Social Question and Answer (SQA) platform (S. Oh, 2018a), adapting it for AI system explanations. The system successfully passed evaluation based on XAI metrics Hoffman, Mueller, et al. (2018), as implemented in a master’s thesis by Mamun (2021), which validated its effectiveness in a basic image classification domain and explored the types of explanations it generated. This Ph.D. dissertation builds upon this prior work, aiming to apply it in a novel context: users and potential users of self-driving semi-autonomous vehicles. This approach seeks to unravel communication patterns within a social QA platform (S. Oh, 2018a), the types of questions it can assist with, and the benefits it might offer users of widely adopted AI systems. Initially, the feasibility of using existing social QA platforms as explanatory tools for an existing AI system was investigated. The study found that users on these platforms collaboratively assist one another in problem-solving, with many resolutions being reached (Linja et al., 2022). An intriguing discovery was that anger directed at the AI system drove increased engagement on the platform. The subsequent phase leverages observations from social QA platforms in the autonomous driving (AD) sector to gain insights into an AI system within a vehicle. The dissertation includes two simulation studies employing these observations as training materials. The studies explore users\u27 Level 3 Situational Awareness (Endsley, 1995) when the autonomous vehicle exhibits abnormal behavior. These investigate detection rates and users\u27 comprehension of abnormal driving situations. Additionally, these studies measure the perception of personalization within the context of the training process (Zhang & Curley, 2018), cognitive workload (Hart & Staveland, 1988), trust, and reliance (Körber, 2018) concerning the training process. The findings from these studies are mixed, showing higher detection rates of abnormal driving with training but diminished trust and reliance. The final study engages current Tesla FSD users in semi-structured interviews (Crandall et al., 2006) to explore their use of social QA platforms, their knowledge sources during the training phase, and their search for answers to abnormal driving scenarios. The results reveal extensive collaboration through social forums and group discussions, shedding light on differences in trust and reliance within this domain