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To solve the problem of low efficiency caused by the heavy traffic in the gas station at the peak time, a method for real-time vehicle detection and tracking using Adaboosting classifier and optical flow tracking is proposed in this paper. The Adaboosting algorithm is used to train the classifier with Haar-like feature extracted from positive samples and negative samples of the gas station vehicles...
Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle...
Pedestrian motion type classification is proposed in this work. The model incorporates the pedestrian pose recognition and lateral speed, motion direction and spatial layout of the environment. Pedestrian poses are recognized according to the spatial body language ratio. The center of mass of the body relative to its width and height is used to define the pedestrian pose. Motion trajectory is obtained...
Moving vehicle detection in dynamical scene is a significant but challenging problem in these days. A new and effective approach to extract moving vehicles is proposed in this paper. In our method, Harris corner and Lucas-Kanade (L-K) optical flow was adopted to generalize feature-point optical flow field between two consecutive frames which obtained from monocular moving camera, and then vector quantization...
We present a hover stabilization system for a quad-rotor MAV. The stabilizer uses a downward-facing CMOS camera and a pyramidal Lucas-Kanade optical flow algorithm running on an embedded computer-on-module to detect self-motion and stabilize laterally. Using this system, the vehicle is able to hover within 20 centimeters of its original location for over five minutes above a variety of surfaces, including...
In this paper, a network-based algorithm is proposed for group activity recognition with multiple people/objects and crowded scenes. This algorithm models the entire scene as an error-free network. With this network, we model objects in the scene as packages while activities as package transmission in the network. By analyzing these “package transmission” processes, activities can be detected. Based...
The driving support system is most important research areas in intelligent transport system (ITS). Moreover, obstacle detection is one of the key technologies, and we have proposed such system based on stereovision system. Additionally, to assist driving safely, it is necessary to extract dynamic objects and alert driver faster. In our previous report, we proposed dynamic objects extraction method...
We present a novel approach for vehicle detection in urban surveillance videos, capable of handling unstructured and crowded environments with large occlusions, different vehicle shapes, and environmental conditions such as lighting changes, rain, shadows, and reflections. This is achieved with virtually no manual labeling efforts. The system runs quite efficiently at an average of 66Hz on a conventional...
The camera of traffic system takes numerous photos every day. It's critically important for judging kinds of car movements and intelligent transportation to pick up the outlines of automobiles and fix their positions. However, the popular car positioning algorithm is unable to reach the required fast speed in this field. In this paper we will provide readers with an algorithm to locate cars based...
This paper presents a video vehicle method that combines local binary pattern and motion histogram. First, use LBP modeling and updating the background in video image. Second, detect the video vehicles by means of the motion histogram. Finally, eliminates shadows from the detected vehicle region, and improves accuracy of vehicle detection. Experiments in some vehicle database show that our method...
In this paper we propose an approach for dynamic scene perception from a moving vehicle equipped with a stereo camera rig. The approach is solely based on visual information, hence it is applicable to a large class of autonomous robots working in indoor as well as in outdoor environments. The proposed approach consists of an egomotion estimation based on disparity and optical flow using the Longuet-Higgins-Equations...
This paper proposes a new approach to describe traffic scene including vehicle collisions and vehicle anomalies at intersections by video processing and motion statistic techniques. The research mainly targets on extracting abnormal event characteristics at intersections and learning normal traffic flow by trajectory clustering techniques. Detecting and analyzing accident events are done by observing...
This paper proposes a statistical rejection rule, designed for small baseline stereo satellites. The method learns an a contrario model for image blocks and discards the casual matches between the images of the stereo pair. A formula estimating the expected number of false alarms under the background model is proved. Comparative experiments on quasi-simultaneous stereo in aerial imagery demonstrate...
Event detection is an important research in video surveillance technology. This paper proposed a method for traffic event detection based on visual Mechanism on the background of traffic video surveillance applications. In this method, based on the extraction of video target motion characteristics, it extracted abnormal targets mainly through the features merging and significant competitive in video...
Combing with specific temporal information of video, this paper proposes a kind of video object tracking method based on normalized cross-correlation matching by using the high precision characteristics of normalized cross-correlation image matching. Firstly, extract video background from the temporal information of video. Then, acquire the region of moving object using background subtraction. Lastly,...
It proposes a SIFT-RANSAC algorithm for the object recognition of moving vehicles combining the SIFT image matching theory and RANSAC algorithm. The paper designs a detection system, the Intelligent Traffic Management, which is used to solve the difficulties, such as traffic lanes changes, large body sizes and mutual interference between vehicles and so on, in the previous frame and the update frame...
A new method for real-time detection and tracking of multiple moving vehicles from traffic video is proposed. This method first uses MoG and texture based model to extract foreground from the scene, then detect moving targets using a modified version of timed motion history image (tMHI), and finally uses Kalman prediction filter to track these targets, which the full moving trajectories of the targets...
This work aims at detecting and tracking vehicles in in-car video. Rather than enhancing shape analysis of various vehicle types and road situations, this work focuses on vehicle and background motions because they are more general than shapes and colors of cars in various road environments. Basic features are tracked stably using corners, intensity peaks, and horizontal line segments. We use the...
We present and evaluate a novel scene descriptor for classifying urban traffic by object motion. Atomic 3D flow vectors are extracted and compensated for the vehicle's egomotion, using stereo video sequences. Votes cast by each flow vector are accumulated in a bird's eye view histogram grid. Since we are directly using low-level object flow, no prior object detection or tracking is needed. We demonstrate...
This paper presents a novel approach for road course estimation on rural roads using a mulitlayer laser scanner. The measurements of the sensor are used to build an occupancy grid as a representation of a local map. This mapping step uses a new free space function and a novel method for detecting and eliminating moving objects. Based on this map a feature extraction algorithm yields road border feature...
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