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Vehicle detection is the core function in any Driver Assistant System. Besides the challenge in various environmental conditions, the limitation in execution time and computing power is also critical. This paper proposes a shadow detection step that aims at recognizing the shadow part of the train in various environments (including very tough cases) to accelerate the detection process. We propose...
VD (Vehicle Detection) is one of the main challenges in ADAS. The encountered vehicles can be of different orientations and positions, especially in scenarios such as crossroads. Traditional VD approaches have shown outstanding performance only with vehicle images of limited orientations and positions: head and tail of a vehicle, others such as side views have been shown to be detection challenged...
The applications of computer vision are widely used in traffic monitoring and surveillance. In traffic monitoring, detection of vehicles plays a significant role. Different attributes such as shape, color, size, pose, illumination, shadows, occlusion, background clutter, camera viewing angle, speed of vehicles and environmental conditions pose immense and varying challenges in the detection phase...
To achieve the goal of frontal vehicle detection in night-driving condition, we propose an effective method to detect the red taillights of vehicles. The challenge is that the taillight images captured with automatic exposure typically are overexposed, which makes red color segmentation often erroneous. Instead of customizing the camera hardware to tackle this problem, we combine morphological and...
Lane departure and forward collision detection plays an important role in autonomous driving and commercial driver-assistance systems. This paper presents an integrative approach to vision-based lane departure detection which aims to be as simple as possible to enable the real-time computation while being able to adapt to a variety of highway and urban scenarios on different weather conditions. In...
Several vehicle detection methods in urban traffic scenes, such as vehicle detection method based on symmetrical features, vehicle detection method based on license plate, vehicle detection method based on Gabor features and Support Vector Machines (SVM), and vehicle detection method based on Haar-like features and AdaBoost classifier, are comparatively used in this paper. The theoretical analysis...
The research community has shown significant improvements in both vision-based detection and tracking of vehicles, working towards a high level understanding of on-road maneuvers. Behaviors of surrounding vehicles in a highway environment is found as an interesting starting point, of why this dataset is introduced along with its challenges and evaluation metrics. A vision-based multi-perspective dataset...
Vehicle detection can provide volumes of useful data for city planning and transport management. It has always been a challenging task because of various complicated backgrounds and the relatively small sizes of targets, especially in high resolution satellite images. A novel model called joint-layer deep convolutional neural networks (JLDCNNs), which joins features in the higher layers and the lower...
Vehicle to Vehicle (V2V) communication systems enable vehicles to communicate with each other and use the shared information to make safety related decisions. However, the safety improvement of the current V2V systems only benefits V2V-enabled objects in the V2V network. The Pedestrian Automatic Emergency Braking System (PAEB) can utilize onboard sensors to detect pedestrians and make safety related...
Performance measures and adaptive control methodologies for traffic signal systems currently require intersections to be instrumented with vehicle detectors and communication equipment, which can require substantial engineering resources to deploy and maintain. Recent studies have explored the use of Connected Vehicle (CV) data for signal performance measures at various levels of market penetration,...
In this paper, we present a learning-based brake light classification algorithm for intelligent driver-assistance systems. State-of-the-art approaches apply different image processing techniques with hand-crafted features to determine whether brake lights are on or off. In contrast, we learn a brake light classifier based on discriminative color descriptors and convolutional features fine-tuned for...
Vehicle detection is an essential task in an intelligent vehicle. Despite being a well-studied vision problem, it is unclear how well vehicle detectors generalize to new settings. Specifically, this paper studies the generalization capability of vehicle detectors on a U.S. highway dataset. Two types of models are employed in the experimental analysis, a subcategory aggregate channel features model...
Smart Parking Sensor Network project aims to develop a low cost sensor based parking system to map the usage of parking areas. This system consists of sensor nodes which can detect the occupancy of parking space; relay nodes to communicate between sensor nodes and the server; server application to get data from the relay nodes and send data to mobile application; and a mobile application to display...
The detection of targets in military and security applications involves the usage of sensor systems which consist of a variety of sensors such as seismic, acoustic, magnetic and image ones as well. In order to extract signal features, which characterize particular targets, using of appropriate signal processing algorithms is required. Seismic signals can be considered as nonstationary and nonlinear...
TYAs paper looks at some of the algorithms that can be used for effective detection and tracking of vehicles, in particular for statistical analysis. The main methods for tracking discussed and implemented are blob analysis, optical flow and foreground detection. A further analysis is also done testing two of the techniques using a number of video sequences that include different levels of difficulties.
The capability of automated vehicles with intelligent on-board systems aimed at improving traffic safety has been in constant advance. This paper proposes an integrated module of lane and vehicle detection for a forward collision warning system that can be embedded in an autonomous driving system operating in real time. Integration of lane and vehicle detection provides more precise information for...
Accurate detection and localization of vehicles in aerial images has a wide range of applications including urban planning, military reconnaissance, visual surveillance, and realtime traffic management. Automated detection of vehicles in aerial imagery is a challenging task, due to the density of vehicles on the road, the complexity of the surrounding environment in urban areas, and low spatial resolution...
In this paper, a real-time vehicle detection system is designed and implemented on an FPGA (Field Programmable Gate Array). The system is composed of an infrared camera and an image acquisition and processing board developed by our research team. An FPGA chip and a DSP chip are embedded in the image board as the major calculation units, which make realtime computation possible. First, edge features...
Research on moving vehicle detection based on Video and Image Processing is a significant research topic in image processing and computer vision, and is also a basic and key work in intelligent traffic system field, which directly affects the follow-on object identification and tracking, analysis and understanding of the traffic scene. In this paper detection of moving vehicles has been studied, analyed...
Vehicle detection and recognition from aerial imagery provides useful information for local vehicle volume estimation and traffic monitoring. In this paper, we propose a method that accurately detects vehicles in urban environment using a probabilistic classification method followed by a refinement based on object segments. Both classification and segmentation methods make use of coregistered aerial...
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