7 research outputs found
VT-Former: A Transformer-based Vehicle Trajectory Prediction Approach For Intelligent Highway Transportation Systems
Enhancing roadway safety and traffic management has become an essential focus
area for a broad range of modern cyber-physical systems and intelligent
transportation systems. Vehicle Trajectory Prediction is a pivotal element
within numerous applications for highway and road safety. These applications
encompass a wide range of use cases, spanning from traffic management and
accident prevention to enhancing work-zone safety and optimizing energy
conservation. The ability to implement intelligent management in this context
has been greatly advanced by the developments in the field of Artificial
Intelligence (AI), alongside the increasing deployment of surveillance cameras
across road networks. In this paper, we introduce a novel transformer-based
approach for vehicle trajectory prediction for highway safety and surveillance,
denoted as VT-Former. In addition to utilizing transformers to capture
long-range temporal patterns, a new Graph Attentive Tokenization (GAT) module
has been proposed to capture intricate social interactions among vehicles.
Combining these two core components culminates in a precise approach for
vehicle trajectory prediction. Our study on three benchmark datasets with three
different viewpoints demonstrates the State-of-The-Art (SoTA) performance of
VT-Former in vehicle trajectory prediction and its generalizability and
robustness. We also evaluate VT-Former's efficiency on embedded boards and
explore its potential for vehicle anomaly detection as a sample application,
showcasing its broad applicability
Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems
Path prediction is an essential task for many real-world Cyber-Physical
Systems (CPS) applications, from autonomous driving and traffic
monitoring/management to pedestrian/worker safety. These real-world CPS
applications need a robust, lightweight path prediction that can provide a
universal network architecture for multiple subjects (e.g., pedestrians and
vehicles) from different perspectives. However, most existing algorithms are
tailor-made for a unique subject with a specific camera perspective and
scenario. This article presents Pishgu, a universal lightweight network
architecture, as a robust and holistic solution for path prediction. Pishgu's
architecture can adapt to multiple path prediction domains with different
subjects (vehicles, pedestrians), perspectives (bird's-eye, high-angle), and
scenes (sidewalk, highway). Our proposed architecture captures the
inter-dependencies within the subjects in each frame by taking advantage of
Graph Isomorphism Networks and the attention module. We separately train and
evaluate the efficacy of our architecture on three different CPS domains across
multiple perspectives (vehicle bird's-eye view, pedestrian bird's-eye view, and
human high-angle view). Pishgu outperforms state-of-the-art solutions in the
vehicle bird's-eye view domain by 42% and 61% and pedestrian high-angle view
domain by 23% and 22% in terms of ADE and FDE, respectively. Additionally, we
analyze the domain-specific details for various datasets to understand their
effect on path prediction and model interpretation. Finally, we report the
latency and throughput for all three domains on multiple embedded platforms
showcasing the robustness and adaptability of Pishgu for real-world integration
into CPS applications
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
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications
Vehicle anomaly detection plays a vital role in highway safety applications
such as accident prevention, rapid response, traffic flow optimization, and
work zone safety. With the surge of the Internet of Things (IoT) in recent
years, there has arisen a pressing demand for Artificial Intelligence (AI)
based anomaly detection methods designed to meet the requirements of IoT
devices. Catering to this futuristic vision, we introduce a lightweight
approach to vehicle anomaly detection by utilizing the power of trajectory
prediction. Our proposed design identifies vehicles deviating from expected
paths, indicating highway risks from different camera-viewing angles from
real-world highway datasets. On top of that, we present VegaEdge - a
sophisticated AI confluence designed for real-time security and surveillance
applications in modern highway settings through edge-centric IoT-embedded
platforms equipped with our anomaly detection approach. Extensive testing
across multiple platforms and traffic scenarios showcases the versatility and
effectiveness of VegaEdge. This work also presents the Carolinas Anomaly
Dataset (CAD), to bridge the existing gap in datasets tailored for highway
anomalies. In real-world scenarios, our anomaly detection approach achieves an
AUC-ROC of 0.94, and our proposed VegaEdge design, on an embedded IoT platform,
processes 738 trajectories per second in a typical highway setting. The dataset
is available at
https://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set
A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems
Anomaly detection is a crucial task in complex distributed systems. A
thorough understanding of the requirements and challenges of anomaly detection
is pivotal to the security of such systems, especially for real-world
deployment. While there are many works and application domains that deal with
this problem, few have attempted to provide an in-depth look at such systems.
In this survey, we explore the potentials of graph-based algorithms to identify
anomalies in distributed systems. These systems can be heterogeneous or
homogeneous, which can result in distinct requirements. One of our objectives
is to provide an in-depth look at graph-based approaches to conceptually
analyze their capability to handle real-world challenges such as heterogeneity
and dynamic structure. This study gives an overview of the State-of-the-Art
(SotA) research articles in the field and compare and contrast their
characteristics. To facilitate a more comprehensive understanding, we present
three systems with varying abstractions as use cases. We examine the specific
challenges involved in anomaly detection within such systems. Subsequently, we
elucidate the efficacy of graphs in such systems and explicate their
advantages. We then delve into the SotA methods and highlight their strength
and weaknesses, pointing out the areas for possible improvements and future
works.Comment: The first two authors (A. Danesh Pazho and G. Alinezhad Noghre) have
equal contribution. The article is accepted by IEEE Transactions on Knowledge
and Data Engineerin
Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety
Recently, Smart Video Surveillance (SVS) systems have been receiving more
attention among scholars and developers as a substitute for the current passive
surveillance systems. These systems are used to make the policing and
monitoring systems more efficient and improve public safety. However, the
nature of these systems in monitoring the public's daily activities brings
different ethical challenges. There are different approaches for addressing
privacy issues in implementing the SVS. In this paper, we are focusing on the
role of design considering ethical and privacy challenges in SVS. Reviewing
four policy protection regulations that generate an overview of best practices
for privacy protection, we argue that ethical and privacy concerns could be
addressed through four lenses: algorithm, system, model, and data. As an case
study, we describe our proposed system and illustrate how our system can create
a baseline for designing a privacy perseverance system to deliver safety to
society. We used several Artificial Intelligence algorithms, such as object
detection, single and multi camera re-identification, action recognition, and
anomaly detection, to provide a basic functional system. We also use
cloud-native services to implement a smartphone application in order to deliver
the outputs to the end users
Understanding Policy and Technical Aspects of AI-enabled Smart Video Surveillance to Address Public Safety
Abstract Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48% to 73.72% in anomaly detection and 96% to 83.07% in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds