30,073 research outputs found
A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
Population growth in large cities has contributed to the increase in vehicles' number, leading to the traffic congestion problem. Incompetent traffic supervision could squander an inconsiderable number of man-hours and might lead to fatal consequences. Therefore, intelligent traffic surveillance systems have to carry more significant roles in highway monitoring and traffic management system throughout the years. Although vehicle detection and classification methods have evolved rapidly throughout the years, they
still lack high-level reasoning. Accurate and precise vehicle recognition and classification are still insufficient to develop an intelligent and reliable traffic system. There is a demand to increase the confidence in image understanding and effectively extract the images
conformed to human perception and without human interference. This paper attempts to summarize a review on several methods that semantically extract and analyze traffic density with image processing techniques. Three (3) methods that have been selected to be discussed in this paper are semantic analysis of traffic video using image understanding, mining semantic context details of traffic scene, and integrating vision and language in semantic description of traffic events from image sequences. Each method is discussed thoroughly, and their outstanding issue is deliberated in this paper
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Because of their recent introduction, self-driving cars and advanced driver
assistance system (ADAS) equipped vehicles have had little opportunity to
learn, the dangerous traffic (including near-miss incident) scenarios that
provide normal drivers with strong motivation to drive safely. Accordingly, as
a means of providing learning depth, this paper presents a novel traffic
database that contains information on a large number of traffic near-miss
incidents that were obtained by mounting driving recorders in more than 100
taxis over the course of a decade. The study makes the following two main
contributions: (i) In order to assist automated systems in detecting near-miss
incidents based on database instances, we created a large-scale traffic
near-miss incident database (NIDB) that consists of video clip of dangerous
events captured by monocular driving recorders. (ii) To illustrate the
applicability of NIDB traffic near-miss incidents, we provide two primary
database-related improvements: parameter fine-tuning using various near-miss
scenes from NIDB, and foreground/background separation into motion
representation. Then, using our new database in conjunction with a monocular
driving recorder, we developed a near-miss recognition method that provides
automated systems with a performance level that is comparable to a human-level
understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition,
61.3% vs. 78.7% at near-miss detection).Comment: Accepted to ICRA 201
Learning Behavioural Context
The original publication is available at www.springerlink.co
Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions
Preparation of high-quality datasets for the urban scene understanding is a
labor-intensive task, especially, for datasets designed for the autonomous
driving applications. The application of the coarse ground truth (GT)
annotations of these datasets without detriment to the accuracy of semantic
image segmentation (by the mean intersection over union - mIoU) could simplify
and speedup the dataset preparation and model fine tuning before its practical
application. Here the results of the comparative analysis for semantic
segmentation accuracy obtained by PSPNet deep learning architecture are
presented for fine and coarse annotated images from Cityscapes dataset. Two
scenarios were investigated: scenario 1 - the fine GT images for training and
prediction, and scenario 2 - the fine GT images for training and the coarse GT
images for prediction. The obtained results demonstrated that for the most
important classes the mean accuracy values of semantic image segmentation for
coarse GT annotations are higher than for the fine GT ones, and the standard
deviation values are vice versa. It means that for some applications some
unimportant classes can be excluded and the model can be tuned further for some
classes and specific regions on the coarse GT dataset without loss of the
accuracy even. Moreover, this opens the perspectives to use deep neural
networks for the preparation of such coarse GT datasets.Comment: 10 pages, 6 figures, 2 tables, The Second International Conference on
Computer Science, Engineering and Education Applications (ICCSEEA2019) 26-27
January 2019, Kiev, Ukrain
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling factor for
a wide range of applications. Object detection has benefited enormously from
large-scale datasets, especially in the context of deep learning. For semantic
urban scene understanding, however, no current dataset adequately captures the
complexity of real-world urban scenes.
To address this, we introduce Cityscapes, a benchmark suite and large-scale
dataset to train and test approaches for pixel-level and instance-level
semantic labeling. Cityscapes is comprised of a large, diverse set of stereo
video sequences recorded in streets from 50 different cities. 5000 of these
images have high quality pixel-level annotations; 20000 additional images have
coarse annotations to enable methods that leverage large volumes of
weakly-labeled data. Crucially, our effort exceeds previous attempts in terms
of dataset size, annotation richness, scene variability, and complexity. Our
accompanying empirical study provides an in-depth analysis of the dataset
characteristics, as well as a performance evaluation of several
state-of-the-art approaches based on our benchmark.Comment: Includes supplemental materia
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