We demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform and effective denial-of-service (DoS) attack, can be utilized to generate adversarial data for video anomaly detection systems.
While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score.
While video action recognition has been an active area of research for several years, zero-shot action recognition has only recently started gaining traction.
The experimental results show that the existing state-of-the-art methods are not suitable for the considered practical challenges, and the proposed algorithm outperforms them with a large margin in continual learning and few-shot learning tasks
We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic.
Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors.
Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate.
In this paper, we propose a fast unsupervised anomaly detection system comprising of three modules: preprocessing module, candidate selection module and backtracking anomaly detection module.
Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet.
Anomaly detection in surveillance videos has been recently gaining attention.
Even though the performance of state-of-the-art methods on publicly available data sets has been competitive, they demand a massive amount of training data.
Ranked #12 on Anomaly Detection on ShanghaiTech
Object detection and recognition has been an ongoing research topic for a long time in the field of computer vision.