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Online Vehicle
Detection using Haar-like, LBP and HOG Feature based Image
Classifiers with Stereo Vision Pre-selection

Environment sensing is an essential property for autonomous cars. With the help of sensors,
nearby objects can be detected
and localized. Furthermore, the creation of an accurate model of the surroundings is crucial for high level planning.
In this paper, we focus on vehicle detection
based on stereo camera images. While
stereoscopic computer vision is
applied to localize objects in the environment, the objects are then identified by image
classifiers. We implemented
and evaluated several algorithms from
image based pattern recognition
in our autonomous car framework, using HOG-, LBP-, and Haar-like features. We will present experimental results using real traffic data with focus on
classification accuracy and
execution times

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Real-time vehicle detection with foreground based cascade
The strategy based on Haar-like features and the cascade classifier for vehicle
detection systems has captured growing attention for its effectiveness and
robustness; however, such a vehicle detection strategy relies on exhaustive scanning
of an entire image with different sizes sliding windows, which is tedious and
inefficient, since a vehicle only occupies a small part of the whole scene.
Therefore, the authors propose a real-time vehicle detection algorithm which
is based on the improved Haar-like
features and combines motion detection with a cascade of classifiers. They
adopt a visual background extractor, accompanied by morphological processing,
to obtain foregrounds. These foregrounds retain vehicle features and provide
the positions within images where vehicles are most likely to be located. Subsequently,
vehicle detection is performed only at these positions by using a cascade of
classifiers instead of a single strong classifier, which is able to improve the
detection performance. The authors’ algorithm has been successfully evaluated
on the public datasets, which demonstrates its robustness and real-time


Real-time Moving Vehicle Detection, Tracking, and Counting System
Implemented with OpenCV

 Moving vehicle detection, tracking, and
counting are very critical for traffic flow monitoring, planning, and controlling.
Video-based solution, comparing to other techniques, does not disturb traffic
flow and is easily installed. By analyzing the traffic video sequence recorded
from a video camera, this paper presents a video-based solution applied with adaptive
subtracted background technology in combination with virtual detector and blob tracking technologies. Experimental results,
implemented in Visual C++ code with
OpenCV development kits, indicate that the proposed method can detect, track,
and count moving vehicles accurately.


Real-time Vehicle Detection using Haar-SURF Mixed Features and
Gentle AdaBoost Classifier

vehicle detection is one of the key techniques in intelligent driver systems and
has been an active research area in the past years. Considering the high demand
for real-time and robust vehicle detection method, a novel vehicle detection method
has been proposed. This paper presents a real-time vehicle detection algorithm
which uses cascade classifier and Gentle
AdaBoost classifier with Haar-SURF
mixed features. We built up a large database including vehicles and
non-vehicles for training and testing. A pipeline is then presented to solve
the detection problem. Firstly, lane detection is employed to reduce the search
space to a ROI. Secondly, the cascade classifier is applied to generate some
candidates. Finally, the single decision classifier evaluates the candidates
and provides the target vehicle. T he experiments and on-road tests prove it to
be a real-time and robust algorithm. In addition, we demonstrate the
effectiveness and practicability of the algorithm by porting it to an Android


Vehicle Detection and Counting using Haar Feature Based Classifier

 In this paper we
would describe a vehicle detection technique
that can be used for traffic surveillance systems. An intelligent traffic surveillance system,
equipped with electronic
devices, works by communicating with
moving vehicles about traffic
conditions, monitor rules and regulations and avoid collision between cars. Therefore the first
step in this process is
the detection of cars. The system uses
Haar like features for vehicle
detection, which is generally used for face detection. Haar feature-based
cascade classifiers are an
effective object detection method
first proposed by Viola and Jones. It’s a machine learning based technique which uses a set of
positive and negative images for
training purpose. Results show this method is quite fast and effective in detecting cars in real
time CCTV footages.


Vehicle Recognition Based on Saliency Detection and Color

recognition is one of the important parts in the intelligent transportation
system. A fast vehicle recognition method is proposed to improve the
performance of the vehicle recognition. Firstly, the detected image is pre-processed
by the image saliency detection based on local features, which can eliminate a
large number of background information to highlight the vehicle image. Then,
the vehicle image which is converted color space from RGB to HSV, and the H
component is counted by color histogram
which is used to identify the vehicle. Finally, the positions of the vehicles
are determined. The simulation results show that the method can detect the
vehicles from the complex background.


A System for
Real-time Detection and Tracking of Vehicles from a Single
Car-mounted Camera— A novel system
for detection and tracking of vehicles
from a single car-mounted camera is presented. The core of the system are high-performance
vision algorithms: the Wald-Boost
detector and the TLD tracker that are scheduled so that a real-time performance is achieved. For a wide range of distances, the recall and precision of detection for cars
are excellent. Statistics for trucks
are also reported. The dataset with
the ground truth is made public.

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