24,278 research outputs found
Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand
Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications
Real-time automated road, lane and car detection for autonomous driving
In this paper, we discuss a vision based system for autonomous guidance of vehicles. An autonomous intelligent vehicle has to perform a number of
functionalities. Segmentation of the road, determining the boundaries to drive in and recognizing the vehicles and obstacles around are the main tasks for vision guided vehicle navigation. In this article we propose a set of algorithms which lead to the solution of road and vehicle segmentation using data from a color camera. The algorithms described here combine gray value difference
and texture analysis techniques to segment the road from the image, several geometric transformations and contour processing algorithms are used to segment lanes, and moving cars are extracted with the help of background modeling and estimation. The techniques developed have been tested in real road images and the results are presented
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic images and videos contain vehicles with a large variance of
scales. In this paper, we delve into the source of scale sensitivity, and
reveal two key issues: 1) existing RoI pooling destroys the structure of small
scale objects, 2) the large intra-class distance for a large variance of scales
exceeds the representation capability of a single network. Based on these
findings, we present a scale-insensitive convolutional neural network (SINet)
for fast detecting vehicles with a large variance of scales. First, we present
a context-aware RoI pooling to maintain the contextual information and original
structure of small scale objects. Second, we present a multi-branch decision
network to minimize the intra-class distance of features. These lightweight
techniques bring zero extra time complexity but prominent detection accuracy
improvement. The proposed techniques can be equipped with any deep network
architectures and keep them trained end-to-end. Our SINet achieves
state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on
the KITTI benchmark and a new highway dataset, which contains a large variance
of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems
(T-ITS
An Intelligent Monitoring System of Vehicles on Highway Traffic
Vehicle speed monitoring and management of highways is the critical problem
of the road in this modern age of growing technology and population. A poor
management results in frequent traffic jam, traffic rules violation and fatal
road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to
address this problem is time-consuming, expensive and tedious. This paper
presents an efficient framework to produce a simple, cost efficient and
intelligent system for vehicle speed monitoring. The proposed method uses an HD
(High Definition) camera mounted on the road side either on a pole or on a
traffic signal for recording video frames. On the basis of these frames, a
vehicle can be tracked by using radius growing method, and its speed can be
calculated by calculating vehicle mask and its displacement in consecutive
frames. The method uses pattern recognition, digital image processing and
mathematical techniques for vehicle detection, tracking and speed calculation.
The validity of the proposed model is proved by testing it on different
highways.Comment: 5 page
Detect the unexpected: a science for surveillance
Purpose – The purpose of this paper is to outline a strategy for research development focused on addressing the neglected role of visual perception in real life tasks such as policing surveillance and command and control settings. Approach – The scale of surveillance task in modern control room is expanding as technology increases input capacity at an accelerating rate. The authors review recent literature highlighting the difficulties that apply to modern surveillance and give examples of how poor detection of the unexpected can be, and how surprising this deficit can be. Perceptual phenomena such as change blindness are linked to the perceptual processes undertaken by law-enforcement personnel. Findings – A scientific programme is outlined for how detection deficits can best be addressed in the context of a multidisciplinary collaborative agenda between researchers and practitioners. The development of a cognitive research field specifically examining the occurrence of perceptual “failures” provides an opportunity for policing agencies to relate laboratory findings in psychology to their own fields of day-to-day enquiry. Originality/value – The paper shows, with examples, where interdisciplinary research may best be focussed on evaluating practical solutions and on generating useable guidelines on procedure and practice. It also argues that these processes should be investigated in real and simulated context-specific studies to confirm the validity of the findings in these new applied scenarios
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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