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Fatality due to road accidents are increasing with the increase in population and number of vehicles. Intelligent systems are developed to counter act the loss due to road accidents. The paper proposes one such method to counter the accidents by the implementation of pedestrian detection by the use of LBP histogram and HAAR-like features. LBP histogram are used for cross checking the HAAR-like features...
Vehicle and Pedestrian Detection is a key problem in computer vision, with several applications including robotics, surveillance and automotive safety. Much of the progress of the past few years has been driven by the availability of challenging public datasets. In this paper, we build up a vehicle and pedestrian detection system by combing Histogram of Oriented Gradients (HoG) feature and support...
This paper proposes an event detection method using noisy object information. Some events have a close connection with objects, and the objects related to the event often appear with the event in a video. For example, if an event "Grooming an animal" appears in a video, an animal and people should appear in the video. If we detect the objects that have a close connection with the events,...
Unmanned aerial vehicles (UAV) are among the fast growing remote sensing technologies in these last few years. This is mainly because UAVs allow acquiring images characterized by an extremely high spatial resolution and they exhibit an interesting operational flexibility. Taking advantage from these unique characteristics can help in addressing problems typical of the civilian contexts. In particular,...
Advanced Driver Assistance Systems (ADAS) are used for assisting the drivers by providing advice and warnings when necessary. CTA (Cross Traffic Alert) systems are a subset of ADAS used for detecting objects (viz., cars, trucks, pedestrians, static objects etc) by using one or more moving cameras, mounted on a vehicle. Usually, CTA systems can detect moving objects within region of interest (ROI)...
This paper proposes Relational HOG (R-HOG) features for object detection, and binary selection by using a wild-card “*” with Real AdaBoost. HOG features are effective for object detection, but their focus on local regions makes them high-dimensional features. To reduce the memory required for the HOG features, this paper proposes a new feature, R-HOG, which creates binary patterns from the HOG features...
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...
Very large format video or wide-area motion imagery (WAMI) acquired by an airborne camera sensor array is characterized by persistent observation over a large field-of-view with high spatial resolution but low frame rates (i.e. one to ten frames per second). Current WAMI sensors have sufficient coverage and resolution to track vehicles for many hours using just a single airborne platform. We have...
In this paper, we propose an object detection method that uses Joint features combined from multiple Histograms of Oriented Gradients (HOG) feature using two-stage boosting. There has been much research in recent years on statistical training methods and object detection methods that combine low-level features obtained from local areas. In our approach, multiple low-level HOG features are combined...
We present a new multi-stage algorithm for car and truck detection from a moving vehicle. The algorithm performs a search for pertinent features in three dimensions, guided by a ground plane and lane boundary estimation sub-system, and assembles these features into vehicle hypotheses. A number of classifiers are applied to the hypotheses in order to remove false detections. Quantitative analysis on...
The paper introduces background extractions with self-adaptive update algorithm and puts forward an improved algorithm based on histogram statistic combining with multi-frame average. It avoids the image trail-blur phenomena using pure multi-frame method on traffic jam, and has relative low computation complexity comparing with the hybrid Gauss model. It can run on the TI DM642 DSP hardware platform,...
Recently video surveillance techniques have been widely applied to intelligent transportation systems. Tracking of moving objects such as vehicles has become a major topic in video surveillance applications. This paper presents a multi-feature fusion model based on a particle filter for moving object tracking. The particle filter combines color and edge orientation information by a stochastic fusion...
Focusing on vehicle detection system, multi-vehicles are detected as a single target, disturbed by cast shadows. No matter what illumination, or if there are shadows, the luminance of the vehicle bottom decreased, the area is the darkest region of an image, is an important information for vehicle detection. Our results show the self-adaptive for the illumination variance, and low ratio of miss detection...
In this paper, we present a new feature to model a class of events that consist of complex interactions among multiple entities captured by tracks and inter-object relationships over space and time. Existing approaches represent these events using features that measure only pairwise relationships between entities at a time, such as relative distance and relative speed. Due to the limitations of the...
Road detection is one of the key issues for autonomous driving. In this paper, we present a drivable road region detection method based on homography estimation with road appearance and driving state models. In the method, the planar road region is detected and objects inside the region are localized through a 2D projective transformation between the stereo image pair by computing the homography induced...
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