2,431 research outputs found
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
Vehicle Detection and Tracking Techniques: A Concise Review
Vehicle detection and tracking applications play an important role for
civilian and military applications such as in highway traffic surveillance
control, management and urban traffic planning. Vehicle detection process on
road are used for vehicle tracking, counts, average speed of each individual
vehicle, traffic analysis and vehicle categorizing objectives and may be
implemented under different environments changes. In this review, we present a
concise overview of image processing methods and analysis tools which used in
building these previous mentioned applications that involved developing traffic
surveillance systems. More precisely and in contrast with other reviews, we
classified the processing methods under three categories for more clarification
to explain the traffic systems
Vision-based Detection, Tracking and Classification of Vehicles using Stable Features with Automatic Camera Calibration
A method is presented for segmenting and tracking vehicles on highways using a camera that is relatively low to the ground. At such low angles, 3D perspective effects cause significant appearance changes over time, as well as severe occlusions by vehicles in neighboring lanes. Traditional approaches to occlusion reasoning assume that the vehicles initially appear well-separated in the image, but in our sequences it is not uncommon for vehicles to enter the scene partially occluded and remain so throughout. By utilizing a 3D perspective mapping from the scene to the image, along with a plumb line projection, a subset of features is identified whose 3D coordinates can be accurately estimated. These features are then grouped to yield the number and locations of the vehicles, and standard feature tracking is used to maintain the locations of the vehicles over time. Additional features are then assigned to these groups and used to classify vehicles as cars or trucks. The technique uses a single grayscale camera beside the road, processes image frames incrementally, works in real time, and produces vehicle counts with over 90% accuracy on challenging sequences. Adverse weather conditions are handled by augmenting feature tracking with a boosted cascade vehicle detector (BCVD). To overcome the need of manual camera calibration, an algorithm is presented which uses BCVD to calibrate the camera automatically without relying on any scene-specific image features such as road lane markings
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Autonomous harvesting and transportation is a long-term goal of the forest
industry. One of the main challenges is the accurate localization of both
vehicles and trees in a forest. Forests are unstructured environments where it
is difficult to find a group of significant landmarks for current fast
feature-based place recognition algorithms. This paper proposes a novel
approach where local observations are matched to a general tree map using the
Delaunay triangularization as the representation format. Instead of point cloud
based matching methods, we utilize a topology-based method. First, tree trunk
positions are registered at a prior run done by a forest harvester. Second, the
resulting map is Delaunay triangularized. Third, a local submap of the
autonomous robot is registered, triangularized and matched using triangular
similarity maximization to estimate the position of the robot. We test our
method on a dataset accumulated from a forestry site at Lieksa, Finland. A
total length of 2100\,m of harvester path was recorded by an industrial
harvester with a 3D laser scanner and a geolocation unit fixed to the frame.
Our experiments show a 12\,cm s.t.d. in the location accuracy and with
real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and
speed limit is realistic during forest operations
Non-overlapping multi-camera detection and tracking of vehicles in tunnel surveillance
We propose a real-time multi-camera tracking approach to follow vehicles in a tunnel surveillance environment with multiple non-overlapping cameras. In such system, vehicles have to be tracked in each camera and passed correctly from one camera to another through the tunnel. This task becomes extremely difficult when intra-camera errors are accumulated. Most typical issues to solve in tunnel scenes are due to low image quality, poor illumination and lighting from the vehicles. Vehicle detection is performed using Adaboost detector, speeded up by separating different cascades for cars and trucks improving general accuracy of detection. A Kalman Filter with two observations, given by the vehicle detector and an averaged optical flow vector, is used for single-camera tracking. Information from collected tracks is used for feeding the inter-camera matching algorithm, which measures the correlation of Radon transform-like projections between the vehicle images. Our main contribution is a novel method to reduce the false positive rate induced by the detection stage. We impose recall over precision in the detection correctness, and identify false positives patterns which are then included subsequently in a high-level decision making step. Results are presented for the case of 3 cameras placed consecutively in an inter-city tunnel. We demonstrate the increased tracking performance of our method compared to existing Bayesian filtering techniques for vehicle tracking in tunnel surveillance
Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring
Jose Manuel Milla, Sergio Luis Toral, Manuel Vargas and Federico Barrero (2010). Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring, Urban Transport and Hybrid Vehicles, Seref Soylu (Ed.), ISBN: 978-953-307-100-8, InTech, DOI: 10.5772/10179. Available from: http://www.intechopen.com/books/urban-transport-and-hybrid-vehicles/computer-vision-techniques-for-background-modeling-in-urban-traffic-monitoringIn this chapter, several background modelling techniques have been described, analyzed and tested. In particular, different algorithms based on sigma-delta filter have been considered due to their suitability for embedded systems, where computational limitations affect a real-time implementation. A qualitative and a quantitative comparison have been performed among the different algorithms. Obtained results show that the sigma-delta algorithm with confidence measurement exhibits the best performance in terms of adaptation to particular specificities of urban traffic scenes and in terms of computational requirements. A prototype based on an ARM processor has been implemented to test the different versions of the sigma-delta algorithm and to illustrate several applications related to vehicle traffic monitoring and implementation details
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
The assumption of scene rigidity is typical in SLAM algorithms. Such a strong
assumption limits the use of most visual SLAM systems in populated real-world
environments, which are the target of several relevant applications like
service robotics or autonomous vehicles. In this paper we present DynaSLAM, a
visual SLAM system that, building over ORB-SLAM2 [1], adds the capabilities of
dynamic object detection and background inpainting. DynaSLAM is robust in
dynamic scenarios for monocular, stereo and RGB-D configurations. We are
capable of detecting the moving objects either by multi-view geometry, deep
learning or both. Having a static map of the scene allows inpainting the frame
background that has been occluded by such dynamic objects. We evaluate our
system in public monocular, stereo and RGB-D datasets. We study the impact of
several accuracy/speed trade-offs to assess the limits of the proposed
methodology. DynaSLAM outperforms the accuracy of standard visual SLAM
baselines in highly dynamic scenarios. And it also estimates a map of the
static parts of the scene, which is a must for long-term applications in
real-world environments.Comment: This work has been accepted at IEEE Robotics and Automation Letters,
and will be presented at the IEEE Conference on Intelligent Robots and
Systems 201
Real time vehicle recognition: a novel method for road detection
Knowing the location of the road in an intelligent traffic systems is one of the most used solutions to ease vehicle detection. For this purpose we propose a vehicle recognition algorithm which performs a real time automatic detection of the zones which vehicles occupy. Such algorithm is capable of functioning under extreme conditions such as low resolution, low capture angle and gray scale images.Peer ReviewedPreprin
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