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Pedestrian detection is one of the most challenging and vital tasks of driver assistance systems (DAS). Among several algorithms developed for human detection, histogram of oriented gradients (HOG) followed by support vector machine (SVM) has shown the most promising results. This paper presents a hardware accelerator for real-time pedestrian detection at different scales to fulfill the real-time...
Robust hand detection and classification is one of the most crucial pre-processing steps to support human computer interaction, driver behavior monitoring, virtual reality, etc. This problem, however, is very challenging due to numerous variations of hand images in real-world scenarios. This work presents a novel approach named Multiple Scale Region-based Fully Convolutional Networks (MSRFCN) to robustly...
Hand detection is an important issue in the analysis of drivers activities, assessment of drivers alertness, and subsequent development of driver safety monitoring system. In this work, the hand detection problem is addressed in the deep Convolutional Neural Network (CNN) framework. Hypothesis of hand regions are first generated with high recall rate by AdaBoost detector associated with Aggregated...
We present a new framework for driver assistance system, detecting moving objects in the street scene. Our algorithm supports a wide range of objects including vehicles, cyclists, pedestrian etc. Based on candidate bounding boxes detected by object proposals, our classifier only responds to the objects truly moving, which is more practical for real applications. Using unified features of color, structure...
Compressed domain moving object segmentation and classification plays an important role in many real-time applications, such as video indexing and intelligent video surveillance. Compared with the previous international video coding standards, such as H.264/AVC, HEVC introduces a host of new coding features. Therefore, moving object segmentation and classification directly from HEVC compressed videos...
This paper describes the target detection and tracking architecture used by the Georgia Tech Aerial Robotics team for the American Helicopter Society (AHS) Micro Aerial Vehicle (MAV) challenge. The vision system described enables vision-aided navigation with additional abilities such as target detection and tracking all performed onboard the vehicles computer. The author suggests a robust target tracking...
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...
Fast object detection is the most important part of the unmanned surface vehicles (USV) which make it possible for the USV to avoid the obstacle automatically and navigate autonomously. So, it is necessary to find a fast and accurate object detection method. In practice, the significant difficulty is that the environment is quite complicated which make the object uncertain. The obstacle may be a person,...
Due to aisle, emergency lanes and other functional areas with rare events and lack of monitoring, an algorithm for smart detection of vehicle in illegal parking area was proposed. Firstly, region of interesting was selected. Secondly, Gaussian Mixture Model was used for background construction, and moving objects were detected by background subtraction. Thirdly, morphological processing was used for...
Detecting objects such as humans or vehicles is a central problem in video surveillance. Myriad standard approaches exist for this problem. At their core, approaches consider either the appearance of people, patterns of their motion, or differences from the background. In this paper we build on dense trajectories, a state-of-the-art approach for describing spatio-temporal patterns in video sequences...
In this paper, we describe our system for object tracking over a multiple-camera network task in BigMM Challenge in conjunction with the first IEEE International Conference on Multimedia Big Data (BigMM 2015). We focus on the detection and tracking of pedestrians and vehicles. Based on background modeling, we use HOG and SVM to detect pedestrian and morphological processing to detect vehicle in single...
This paper describes a car detection method by combining data obtained from a laser and a camera. Data from the camera and the laser range finder (LRF) are combined after a calibration method has been performed. The calibration method defines the relative pose between camera and LRF. Car candidates are then extracted from the LRF data. The car candidate regions on the image are generated based on...
Vehicle flow volume on the motorway in the urban area is successfully used to realize traffic decision-making and guidance. In this research, we introduced some critical components into a vehicle flow detection system. In order to enhance the accuracy and instantaneity of the vehicle counting in quick-change traffic conditions, an adaptive modeling and updating method of background was proposed. In...
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.
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...
Deep convolutional Neural Networks (DNN) is the state-of-the-art machine learning method. It has been used in many recognition tasks including handwritten digits, Chinese words and traffic signs, etc. However, training and test DNN are time-consuming tasks. In practical vehicle detection application, both speed and accuracy are required. So increasing the speeds of DNN while keeping its high accuracy...
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,...
In this paper, an improved implicit shape model is presented for on-road vehicle and pedestrian detection. Implicit shape model (ISM) is widely used for object detection and categorization. The training of ISM usually consists of three components: interest point detector, local feature descriptor, codebook generation. We evaluate six common interest point detectors to determine the best detector for...
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)...
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