The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Pedestrian Detection is a critical technique for avoiding the collision between the vehicle and people, and it can be used in the advanced driver assistance system. Most research of the pedestrian detection areas are focused on the standing or walking people at the training process. INRIA's pedestrian dataset is composed of persons standing and facing the front, however another datasets comprise various...
In this paper1, a localization system for a mobile robot is proposed, using a top-down multi-sensorial approach and exploiting a map of the environment. Generally, the data sensors are associated with the map by a classical map-matching process. Because of the embedded sensors, the field of view is limited, there is a risk of false association between map and the sensor data. Popular methods try to...
Multiple Inductive Loop Detectors are advanced Inductive Loop Sensors that can measure traffic flow parameters in even conditions where the traffic is heterogeneous and does not conform to lanes. This sensor consists of many inductive loops in series, with each loop having a parallel capacitor across it. These inductive and capacitive elements of the sensor may undergo open or short circuit faults...
Pedestrian detection (PD) and tracking is one of the most prominent functionalities in ADAS (Advanced Driver Assistance Systems). These systems use a forward looking camera (FLC) to detect pedestrians and warn the driver in the host vehicle for the possibility of collision. In the past decade, multiple pedestrian tracking has been a significant and crucial area of research. This paper describes the...
Object recognition is a wide applied task in computer vision. Many fine-grained object recognition approaches are proposed in recent years to detect the same species objects effectively at subordinate-level. In this paper, we present a novel fine-grained vehicle recognition by utilizing collaborative feedback scheme of detection-classification-tracking in surveillance video. We collect a labeled data...
Traffic jam is one of the big and complex problems which happens in many big cities around the world. The advances in technology have enabled tracking moving objects such as vehicles move on road networks. Those data is called spatio-temporal data. By using data mining techniques, analysis on traffic characteristics can be conducted to detect potential area of traffic jam. This information is particularly...
A novel method for vehicle detection and tracking, which is capable to correctly detect and track vehicle headlights in rural and urban areas is presented. The detection procedure does not require image thresholding, or other preprocessing, which is commonly used in other methods, thus offering robustness and performance. A tracking method based on Joint Probability Data Association Filter (JPDAF)...
Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near- miss incidents using forward facing videos from trains. As near-miss events occur more frequently...
Activities developed under hazardous conditions, such as mining, require an active investment from the industry to avoid serious injuries and even loss of lives. One of the safety measures taken to address this problem is to implement a video surveillance system able to track vehicles and workers to enforce safety protocols preventing dangerous events that could lead to accidents. In this paper, we...
This paper introduces a new approach to design a vehicle detection module using a microwave motion sensor in order to obtain vehicle length and vehicle speed. The design of this module aims to apply for Intelligent Transport System (ITS) in which it plays a role as a unit of Traffic Detectors with Inductive Loops, Video, Infrared Sensors, etc. The module has advantages of using low power, costing...
In visual surveillance, vehicle tracking and identification is very popular and applied in many applications such as traffic incident detection, traffic control and management. Edge detection is the key to the success of vehicle tracking and identification. Edge detection is to identify edge locations or geometrical shape changes in term of pixel value along a boundary of two regions in an image....
Conventionally, Fundamental Diagrams, which consist of vehicle traffic flow and density pairs, are obtained from intrusive sensor such as inductive loop detectors. However these sensors are uncommon in developing countries as they are embedded in the roads, and consequently expensive to deploy and impractical to implement on busy roads. Our novel method, VDZ with CCTV snap shots can provide the data...
Many applications of machine-to-machine (M2M) based intelligent transportation systems highly rely on the accurate estimation of neighbor map, where neighbor map mentions the locations of all nearby vehicles and pedestrians. To build the neighbor map, it usually integrates multiple sensors, such as GPS, odometer, inertial measurement unit (IMU), laser scanners, cameras, and RGB-D cameras. In this...
In the new digital age, the pace and volume of growing transportation related data is exceeding our ability to manage and analyze it. In this position paper, we present a data engine, Godzilla, to ingest real-time traffic data and support analytic and data mining over traffic data. Godzilla is a multi-cluster approach to handle large volumes of growing data, changing workloads and varying number of...
Intelligent “street lighting”, along with its immense energy saving potential, relies upon many factors, not least, the importance of maintaining useable levels of light for both vehicles and pedestrian traffic. One element in the establishment of such a regime is the development of sensory equipment capable of vehicle and human detection with a negligible degree of error. The paper proposes a hybrid...
The purpose of this study is to increase the face detection accuracy in vehicle cabin. Although existing face detectors employed in consumer applications already have sufficient face detection accuracy for many situations, we revealed that detection rate of existing face detector is drastically decreased by shadow on the driver's face caused by sunlight whose relative direction to the driver is continuously...
This paper presents a rapid edge detection method for vehicle using the two-dimensional cellular neural network (CNN). In the method, two adaptive templates for edge detection are experimentally designed, and background noise elimination is also concerned. Finally, the performance of the proposed CNN detector is evaluated on different vehicle images, and it is also compared with some other edge detectors...
Practical surveillance systems deployed in urban scenarios need to operate 24/7 under a wide range of environmental conditions. As modern video analytics shift from blob-based to object-centered architectures, appearance-based object detection under different weather conditions and lighting effects emerges as a critical yet largely unaddressed problem. This paper investigates this research topic,...
As vehicles travel through a scene, changes in aspect ratio and appearance as observed from a camera (or an array of cameras) make vehicle detection a difficult computer vision problem. Rather than relying solely on appearance cues, we propose a framework for detecting vehicles and eliminating false positives by utilizing the motion cues in the scene in addition to the appearance cues. As a case study,...
In this research paper Objects are detected and recognized in cluttered scene. We use Harris Corner Detector to extract interest points, and use additional descriptor FREAK (Fast Retina Keypoint) to match and find detect the object. We also use some classification algorithm to classify and label the object based on the extracted features. The proposed techniques are precise and robust.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.