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Consider to the defect of traditional features which have high empirical components. A vehicle classification algorithm based on the fusion of higher-layer features of a deep network and traditional features was proposed. Firstly, the traditional features of PHOG and LBP-EOH were extracted. Secondly, the higher-layer features excavated from the vehicle pictures by deep belief networks were added,...
We present a TLD-based vehicle tracking method, which uses HOG that is precomputed in the detection process, and an online SVM re-detector. We perform HOG-based tracking in EHMI. When the tracking fails, the system performs redetection for neighboring regions. Tracked vehicles are reused as positive data for re-learning. Therefore, the proposed system performs robust tracking without additional computation.
This paper presents an improved vision-based algorithm for detecting and recognizing vehicle logos in images captured by road surveillance cameras. Vehicle logo recognition is quite a challenging task considering the low resolution of the logos, the wide range of variability in illumination and the interference of the air-intake grille. However, our system, assessed on a set of 1386 vehicle images...
Previous works on outdoor traffic sign recognition and classification have been demonstrated useful to the driver assistant system and the possibility to the autonomous vehicles. This motivates our research on the assistance for visual impairment or visual disabled pedestrians in the indoor environment. In this paper, we build an indoor sign database and investigate the recognition and classification...
Logo identification and classification have received considerable attention from both the machine learning and computer vision communities. Vehicle logo recognition (VLR) is used to recognise accurately the manufacturer of a vehicle by using its iconic logo. A VLR system in addition to license plate recognition aims to increase the confidence of vehicle monitoring systems in private environments such...
In recent years, many vehicle detection algorithms have been proposed. However, a lot of challenges still remain. Local Binary Pattern (LBP) is one of the most popular texture descriptors which has shown its superiority in face recognition and pedestrian detection. But the original LBP pattern is sensitive to noise especially in flat region where gray levels change rarely. To solve this problem, Local...
In this paper we are going to estimate the vehicular traffic density by using acoustic or sound signals. Here we will estimate three probable conditions of traffic that is heavy flow traffic (0-10km/h), medium flow (20-40km/h), and free flow (above 40km/h) traffic. Cumulative sound signals consist of various noises coming from various part of vehicles which includes rotational parts, vibrations in...
In past two decades, developing a system that can navigate vehicle autonomously becomes more interesting problem. The vehicle is equipped by sensors, such as radar, laser, GPS, and camera for sensing the surrounding. Among them, utilization of camera with computer vision technique is the most adopted method for constructing such a system. It is because camera provides a lot of information and is low-cost...
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...
This work aims at automatic detection of man-made pole-like structures in scans of urban environments acquired by a 3D sensor mounted on top a moving vehicle. Pole-like structures, such as e.g. roadsigns and streetlights, are widespread in these environments, and their reliable detection is relevant to applications dealing with autonomous navigation, facility damage detection, city planning and maintenance...
High accuracy pedestrian detection plays an important role in all intelligent vehicles. This paper describes a system for detecting the obstacles in front of the vehicle and classifying them in pedestrians and non-pedestrians. It acquires the traffic scenes using a low-cost pair of gray intensities stereo cameras. A SORT-SGM stereo-reconstruction technique is used in order to obtain high density and...
Despite many years of research, pedestrian recognition is still a difficult, but very important task. We present a multi-modality approach, that combines features extracted from three type of images: intensity, depth and flow. For the feature extraction phase we use Kernel Descriptors, which are optimised independently on each type of image, and for the learning phase we use Support Vector Machines...
Today, video surveillance technology is playing a more and more important role in traffic detection. Vehicle's static properties are crucial information in examining criminal and traffic violations. With the development of Video Surveillance technology, it has been wildly used in the traffic monitoring. Therefore, there is a trend to use Video Surveillance to do intelligent analysis on vehicles. Now,...
A vehicle detection system is realized in two stages: hypothesis generation (HG) and hypothesis verification (HV). HG adopts frame division and shadow detection to find possible candidates of vehicles within a plausible region of the image frame. Then, during HV, object ratio constraint is first used to eliminate unreasonable hypotheses. Afterward, based on the training results of the support vector...
Traffic congestion judgement is a frequently addressed problem in intelligent transportation system. In this paper, a judgement algorithm for identifying the occurring traffic congestion of vehicles is experimentally designed. This algorithm extracts the SIFT features from an image containing vehicles using the linear spatial pyramid matching using sparse coding (ScSPM), then judges wether the congestion...
This paper presents a new approach to recognize the types of moving vehicles in a distributed, wireless sensor network, based on sparse signal representation. Through a sparse representation computed by l1-minimization, we propose a general classification algorithm for acoustic object recognition. This algorithm first uses Mel frequency cepstral coefficients to extract the acoustic features of vehicles...
In order to recognize multi-class vehicles, traditional methods are typically based on license plates and frontal images of vehicles. These methods rely heavily on specific datasets and thus are not applicable in real-world tasks. In this paper, we propose a novel method based on a hierarchical model, HMAX, which simulates visual architecture of primates for object recognition. It can extract features...
The assessment of a drivers' cognitive distraction during driving is a challenging topic in research community. This paper states the ongoing research assessing driver's cognitive distraction using simulated driving. The driver's cognitive distraction detection is based on the analysis of EEG signal. A new analysis method based on Singular Value Decomposition (SVD) is introduced for the extraction...
Animal-Vehicle Collisions (AVCs) have been a challenging problem since the creation of cars. Consequently, such collisions cause hundreds of human and animal deaths, thousands of injuries, and billions of dollars in property damage every year. To cope with this challenge, vehicles have to be equipped with smart systems able to detect animals (e.g., moose), which cross roadways, and warn drivers about...
The proper identification of the traffic signs can ensure driving safety and can play a very important role in reducing the number of road accidents significantly. This paper represents a uniform way to detect the speed limit traffic signs and to confirm it by recognizing the sign's speed number. In this system, firstly the red color objects are segmented from an image using LVQ. Secondly, detected...
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