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

Tracking interacting targets in multi-modal sensors

PhDObject tracking is one of the fundamental tasks in various applications such as surveillance, sports, video conferencing and activity recognition. Factors such as occlusions, illumination changes and limited field of observance of the sensor make tracking a challenging task. To overcome these challenges the focus of this thesis is on using multiple modalities such as audio and video for multi-target, multi-modal tracking. Particularly, this thesis presents contributions to four related research topics, namely, pre-processing of input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking, and interaction recognition. To improve the performance of detection algorithms, especially in the presence of noise, this thesis investigate filtering of the input data through spatio-temporal feature analysis as well as through frequency band analysis. The pre-processed data from multiple modalities is then fused within Particle filtering (PF). To further minimise the discrepancy between the real and the estimated positions, we propose a strategy that associates the hypotheses and the measurements with a real target, using a Weighted Probabilistic Data Association (WPDA). Since the filtering involved in the detection process reduces the available information and is inapplicable on low signal-to-noise ratio data, we investigate simultaneous detection and tracking approaches and propose a multi-target track-beforedetect Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses the detection step and performs tracking in the raw signal. Finally, we apply the proposed multi-modal tracking to recognise interactions between targets in regions within, as well as outside the cameras’ fields of view. The efficiency of the proposed approaches are demonstrated on large uni-modal, multi-modal and multi-sensor scenarios from real world detections, tracking and event recognition datasets and through participation in evaluation campaigns

A comprehensive survey of multi-view video summarization

[EN] There has been an exponential growth in the amount of visual data on a daily basis acquired from single or multi-view surveillance camera networks. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Multi-view video summarization (MVS) is a less redundant and more concise way of providing information from the video content of all the cameras in the form of either keyframes or video segments. This paper presents an overview of the existing strategies proposed for MVS, including their advantages and drawbacks. Our survey covers the genericsteps in MVS, such as the pre-processing of video data, feature extraction, and post-processing followed by summary generation. We also describe the datasets that are available for the evaluation of MVS. Finally, we examine the major current issues related to MVS and put forward the recommendations for future research(1). (C) 2020 Elsevier Ltd. All rights reserved.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067)Hussain, T.; Muhammad, K.; Ding, W.; Lloret, J.; Baik, SW.; De Albuquerque, VHC. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognition. 109:1-15. https://doi.org/10.1016/j.patcog.2020.10756711510

Automatic object classification for surveillance videos.

PhDThe recent popularity of surveillance video systems, specially located in urban scenarios, demands the development of visual techniques for monitoring purposes. A primary step towards intelligent surveillance video systems consists on automatic object classification, which still remains an open research problem and the keystone for the development of more specific applications. Typically, object representation is based on the inherent visual features. However, psychological studies have demonstrated that human beings can routinely categorise objects according to their behaviour. The existing gap in the understanding between the features automatically extracted by a computer, such as appearance-based features, and the concepts unconsciously perceived by human beings but unattainable for machines, or the behaviour features, is most commonly known as semantic gap. Consequently, this thesis proposes to narrow the semantic gap and bring together machine and human understanding towards object classification. Thus, a Surveillance Media Management is proposed to automatically detect and classify objects by analysing the physical properties inherent in their appearance (machine understanding) and the behaviour patterns which require a higher level of understanding (human understanding). Finally, a probabilistic multimodal fusion algorithm bridges the gap performing an automatic classification considering both machine and human understanding. The performance of the proposed Surveillance Media Management framework has been thoroughly evaluated on outdoor surveillance datasets. The experiments conducted demonstrated that the combination of machine and human understanding substantially enhanced the object classification performance. Finally, the inclusion of human reasoning and understanding provides the essential information to bridge the semantic gap towards smart surveillance video systems

Downstream Task Self-Supervised Learning for Object Recognition and Tracking

This dissertation addresses three limitations of deep learning methods in image and video understanding-based machine vision applications. Firstly, although deep convolutional neural networks (CNNs) are efficient for image recognition applications such as object detection and segmentation, they perform poorly under perspective distortions. In real-world applications, the camera perspective is a common problem that we can address by annotating large amounts of data, thus limiting the applicability of the deep learning models. Secondly, the typical approach for single-camera tracking problems is to use separate motion and appearance models, which are expensive in terms of computations and training data requirements. Finally, conventional multi-camera video understanding techniques use supervised learning algorithms to determine temporal relationships among objects. In large-scale applications, these methods are also limited by the requirement of extensive manually annotated data and computational resources.To address these limitations, we develop an uncertainty-aware self-supervised learning (SSL) technique that captures a model\u27s instance or semantic segmentation uncertainty from overhead images and guides the model to learn the impact of the new perspective on object appearance. The test-time data augmentation-based pseudo-label refinement technique continuously trains a model until convergence on new perspective images. The proposed method can be applied for both self-supervision and semi-supervision, thus increasing the effectiveness of a deep pre-trained model in new domains. Extensive experiments demonstrate the effectiveness of the SSL technique in both object detection and semantic segmentation problems. In video understanding applications, we introduce simultaneous segmentation and tracking as an unsupervised spatio-temporal latent feature clustering problem. The jointly learned multi-task features leverage the task-dependent uncertainty to generate discriminative features in multi-object videos. Experiments have shown that the proposed tracker outperforms several state-of-the-art supervised methods. Finally, we proposed an unsupervised multi-camera tracklet association (MCTA) algorithm to track multiple objects in real-time. MCTA leverages the self-supervised detector model for single-camera tracking and solves the multi-camera tracking problem using multiple pair-wise camera associations modeled as a connected graph. The graph optimization method generates a global solution for partially or fully overlapping camera networks

Urban Anomaly Analytics: Description, Detection, and Prediction

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.Peer reviewe