2,618 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
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
Vehicles Recognition Using Fuzzy Descriptors of Image Segments
In this paper a vision-based vehicles recognition method is presented.
Proposed method uses fuzzy description of image segments for automatic
recognition of vehicles recorded in image data. The description takes into
account selected geometrical properties and shape coefficients determined for
segments of reference image (vehicle model). The proposed method was
implemented using reasoning system with fuzzy rules. A vehicles recognition
algorithm was developed based on the fuzzy rules describing shape and
arrangement of the image segments that correspond to visible parts of a
vehicle. An extension of the algorithm with set of fuzzy rules defined for
different reference images (and various vehicle shapes) enables vehicles
classification in traffic scenes. The devised method is suitable for
application in video sensors for road traffic control and surveillance systems.Comment: The final publication is available at http://www.springerlink.co
Illumination invariant stationary object detection
A real-time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation-based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state-of-the-art methods
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping
Many tasks performed by autonomous vehicles such as road marking detection,
object tracking, and path planning are simpler in bird's-eye view. Hence,
Inverse Perspective Mapping (IPM) is often applied to remove the perspective
effect from a vehicle's front-facing camera and to remap its images into a 2D
domain, resulting in a top-down view. Unfortunately, however, this leads to
unnatural blurring and stretching of objects at further distance, due to the
resolution of the camera, limiting applicability. In this paper, we present an
adversarial learning approach for generating a significantly improved IPM from
a single camera image in real time. The generated bird's-eye-view images
contain sharper features (e.g. road markings) and a more homogeneous
illumination, while (dynamic) objects are automatically removed from the scene,
thus revealing the underlying road layout in an improved fashion. We
demonstrate our framework using real-world data from the Oxford RobotCar
Dataset and show that scene understanding tasks directly benefit from our
boosted IPM approach.Comment: equal contribution of first two authors, 8 full pages, 6 figures,
accepted at IV 201
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
In recent years, numerous effective multi-object tracking (MOT) methods are
developed because of the wide range of applications. Existing performance
evaluations of MOT methods usually separate the object tracking step from the
object detection step by using the same fixed object detection results for
comparisons. In this work, we perform a comprehensive quantitative study on the
effects of object detection accuracy to the overall MOT performance, using the
new large-scale University at Albany DETection and tRACking (UA-DETRAC)
benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging
video sequences captured from real-world traffic scenes (over 140,000 frames
with rich annotations, including occlusion, weather, vehicle category,
truncation, and vehicle bounding boxes) for object detection, object tracking
and MOT system. We evaluate complete MOT systems constructed from combinations
of state-of-the-art object detection and object tracking methods. Our analysis
shows the complex effects of object detection accuracy on MOT system
performance. Based on these observations, we propose new evaluation tools and
metrics for MOT systems that consider both object detection and object tracking
for comprehensive analysis.Comment: 18 pages, 11 figures, accepted by CVI
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