1,726 research outputs found
Clustering learning model of CCTV image pattern for producing road hazard meteorological information
BEAGLEBOARD EMBEDDED SYSTEM FOR ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM WITH CAMERA SENSOR
Traffic is one of the most important aspects in human daily life because traffic affects smoothness of capital flows, logistics, and other community activities. Without appropriate traffic light control system, possibility of traffic congestion will be very high and hinder people’s life in urban areas. Adaptive traffic light control system can be used to solve traffic congestions in an intersection because it can adaptively change the durations of green light each lane in an intersection depend on traffic density. The proposed adaptive traffic light control system prototype uses Beagleboard-xM, CCTV camera, and AVR microcontrollers. We use computer vision technique to obtain information on traffic density combining Viola-Jones method with Kalman Filter method. To calculate traffic light time of each traffic light in intersection, we use Distributed Constraint Satisfaction Problem (DCSP). From implementations and experiments results, we conclude that BeagleBoard-xM can be used as main engine of adaptive traffic light control system with 91.735% average counting rate.
Lalu intas adalah salah satu aspek yang paling penting dalam kehidupan sehari-hari manusia karena lalu lintas memengaruhi kelancaran arus modal, logistik, dan kegiatan masyarakat lainnya. Tanpa sistem kontrol lampu lalu lintas yang memadai, kemungkinan kemacetan lalu lintas akan sangat tinggi dan menghambat kehidupan masyarakat di perkotaan. Sistem kontrol lampu lalu lintas adaptif dapat digunakan untuk memecahkan kemacetan lalu lintas di persimpangan karena dapat mengubah durasi lampu hijau di setiap persimpangan jalan tergantung pada kepadatan lalu lintas. Prototipe sistem kontrol lampu lalu lintas menggunakan BeagleBoard-XM, kamera CCTV, dan mikrokontroler AVR. Peneliti menggunakan teknik computer vision untuk mendapatkan informasi tentang kepadatan lalu lintas dengan menggabungkan metode Viola-Jones dan metode Filter Kalman. Untuk menghitung waktu setiap lampu lalu lintas di persimpangan, peneliti menggunakan Distributed Constraint Satisfaction Problem (DCSP). Dari hasil implementasi dan percobaan dapat disimpulkan bahwa BeagleBoard-XM dapat digunakan sebagai mesin utama sistem kontrol lampu lalu lintas adaptif dengan tingkat akurasi penghitungan rata-rata sebesar 91.735%
Computer Vision-based Accident Detection in Traffic Surveillance
Computer vision-based accident detection through video surveillance has
become a beneficial but daunting task. In this paper, a neoteric framework for
detection of road accidents is proposed. The proposed framework capitalizes on
Mask R-CNN for accurate object detection followed by an efficient centroid
based object tracking algorithm for surveillance footage. The probability of an
accident is determined based on speed and trajectory anomalies in a vehicle
after an overlap with other vehicles. The proposed framework provides a robust
method to achieve a high Detection Rate and a low False Alarm Rate on general
road-traffic CCTV surveillance footage. This framework was evaluated on diverse
conditions such as broad daylight, low visibility, rain, hail, and snow using
the proposed dataset. This framework was found effective and paves the way to
the development of general-purpose vehicular accident detection algorithms in
real-time.Comment: Accepted in 10th ICCCNT 201
Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras
Traffic cameras remain the primary source data for surveillance activities
such as congestion and incident monitoring. To date, State agencies continue to
rely on manual effort to extract data from networked cameras due to limitations
of the current automatic vision systems including requirements for complex
camera calibration and inability to generate high resolution data. This study
implements a three-stage video analytics framework for extracting
high-resolution traffic data such vehicle counts, speed, and acceleration from
infrastructure-mounted CCTV cameras. The key components of the framework
include object recognition, perspective transformation, and vehicle trajectory
reconstruction for traffic data collection. First, a state-of-the-art vehicle
recognition model is implemented to detect and classify vehicles. Next, to
correct for camera distortion and reduce partial occlusion, an algorithm
inspired by two-point linear perspective is utilized to extracts the region of
interest (ROI) automatically, while a 2D homography technique transforms the
CCTV view to bird's-eye view (BEV). Cameras are calibrated with a two-layer
matrix system to enable the extraction of speed and acceleration by converting
image coordinates to real-world measurements. Individual vehicle trajectories
are constructed and compared in BEV using two time-space-feature-based object
trackers, namely Motpy and BYTETrack. The results of the current study showed
about +/- 4.5% error rate for directional traffic counts, less than 10% MSE for
speed bias between camera estimates in comparison to estimates from probe data
sources. Extracting high-resolution data from traffic cameras has several
implications, ranging from improvements in traffic management and identify
dangerous driving behavior, high-risk areas for accidents, and other safety
concerns, enabling proactive measures to reduce accidents and fatalities.Comment: 25 pages, 9 figures, this paper was submitted for consideration for
presentation at the 102nd Annual Meeting of the Transportation Research
Board, January 202
Vision-based traffic surveys in urban environments
This paper presents a state-of-the-art, vision-based vehicle detection and type classification to perform traffic surveys from a roadside closed-circuit television camera. Vehicles are detected using background subtraction based on a Gaussian mixture model that can cope with vehicles that become stationary over a significant period of time. Vehicle silhouettes are described using a combination of shape and appearance features using an intensity-based pyramid histogram of orientation gradients (HOG). Classification is performed using a support vector machine, which is trained on a small set of hand-labeled silhouette exemplars. These exemplars are identified using a model-based preclassifier that utilizes calibrated images mapped by Google Earth to provide accurately surveyed scene geometry matched to visible image landmarks. Kalman filters track the vehicles to enable classification by majority voting over several consecutive frames. The system counts vehicles and separates them into four categories: car, van, bus, and motorcycle (including bicycles). Experiments with real-world data have been undertaken to evaluate system performance and vehicle detection rates of 96.45% and classification accuracy of 95.70% have been achieved on this data.The authors gratefully acknowledge the Royal Borough of Kingston for providing the video data. S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de EconomÃa y Competitividad (COFUND2013-51509) and Banco Santander
- …