5,705 research outputs found
CHAD: Charlotte Anomaly Dataset
In recent years, we have seen a significant interest in data-driven deep
learning approaches for video anomaly detection, where an algorithm must
determine if specific frames of a video contain abnormal behaviors. However,
video anomaly detection is particularly context-specific, and the availability
of representative datasets heavily limits real-world accuracy. Additionally,
the metrics currently reported by most state-of-the-art methods often do not
reflect how well the model will perform in real-world scenarios. In this
article, we present the Charlotte Anomaly Dataset (CHAD). CHAD is a
high-resolution, multi-camera anomaly dataset in a commercial parking lot
setting. In addition to frame-level anomaly labels, CHAD is the first anomaly
dataset to include bounding box, identity, and pose annotations for each actor.
This is especially beneficial for skeleton-based anomaly detection, which is
useful for its lower computational demand in real-world settings. CHAD is also
the first anomaly dataset to contain multiple views of the same scene. With
four camera views and over 1.15 million frames, CHAD is the largest fully
annotated anomaly detection dataset including person annotations, collected
from continuous video streams from stationary cameras for smart video
surveillance applications. To demonstrate the efficacy of CHAD for training and
evaluation, we benchmark two state-of-the-art skeleton-based anomaly detection
algorithms on CHAD and provide comprehensive analysis, including both
quantitative results and qualitative examination. The dataset is available at
https://github.com/TeCSAR-UNCC/CHAD
An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos
Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation
Visual anomaly detection is essential and commonly used for many tasks in the
field of computer vision. Recent anomaly detection datasets mainly focus on
industrial automated inspection, medical image analysis and video surveillance.
In order to broaden the application and research of anomaly detection in
unmanned supermarkets and smart manufacturing, we introduce the supermarket
goods anomaly detection (GoodsAD) dataset. It contains 6124 high-resolution
images of 484 different appearance goods divided into 6 categories. Each
category contains several common different types of anomalies such as
deformation, surface damage and opened. Anomalies contain both texture changes
and structural changes. It follows the unsupervised setting and only normal
(defect-free) images are used for training. Pixel-precise ground truth regions
are provided for all anomalies. Moreover, we also conduct a thorough evaluation
of current state-of-the-art unsupervised anomaly detection methods. This
initial benchmark indicates that some methods which perform well on the
industrial anomaly detection dataset (e.g., MVTec AD), show poor performance on
our dataset. This is a comprehensive, multi-object dataset for supermarket
goods anomaly detection that focuses on real-world applications.Comment: 8 pages, 6 figure
An Advanced Home ElderCare Service
With the increase of welfare cost all over the developed world, there is a need to resort to new technologies
that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a
middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed
at home for emergency detection and provides a framework for creating and managing rescue teams willing
to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service
Autonomous real-time surveillance system with distributed IP cameras
An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image
processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects
moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator
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