VEHICLE TRACKING
The proposed tracking framework designed as: Tracker estimates the
motion of vehicle or vehicles between the frame sequences. Detector
processes in each frame independently and localise the target vehicle or
vehicles based on the training classifier. The training classifier
updates constantly from the learning process. The learning component
also estimates the errors of the detector which it can make two types of
errors: the false positive and false negative. In addition, the
learning component also can generate positive and negative training
samples based on the error estimation for the future detection to avoid
errors. It is assumed that both detector and tracker can make errors so
FBT has been proposed to monitoring the performance of the tracker. By
using the proposed method, more training samples based on the current
input video can be generated which the classifier will be updated more
accurate. Code Shoppy
Vehicle tracking methods can use various features, such as points
[5], models [6], shapes [7], and motions [8]. This paper focuses on
using the points and the motions of the targets. Window tracking is a
widely used in object tracking and there are two approaches in the
window tracking process: static template model [9] and adaptive model
[10]. The main difference between them is that the adaptive model can
update the template during the tracking process and the other is not.
However, the disadvantage of the window tracking is that the templates
are limited for appearance modelling. In this process, an adaptive
discriminative tracking model has proposed, which the model template of
the targets are updated continually in both offline and during the
process. The positive results in the neighbourhood frames by the
tracking process are used to be the positive training samples in the
following detection and tracking process, similarly, the negative
results are used as negative training samples. The update strategy can
handle the problems of changing appearance of the target and short-term
occlusion which is another problem in tracking as tracking will be
affected by any frames lost or random similar appearances of background
during tracking. The TLD [4] algorithm built an online feature detector
of a single target at the first frame, which can search the target
continuously during the entire tracking process. Positive and negative
samples are generated for update the detector classification model. This
approach addresses the problem of recovering the tracking target in the
event of tracking failures but it can only track the area selected in
the first frame by the operator. Appearance-based and motion-based are
the methods of the vehicle detection. Appearance-based methods recognize
vehicles directly from a single image and the motion-based methods
require a set of sequenced images or frames of a video in order to
recognize vehicles. Most of the literatures used appearance-based
methods because this method can detect vehicles from a single image
rather than sequenced frames. In this paper, the vehicle detection is
applied on flying UAVs so the stationary vehicles cannot be detected
from the background by the motion-based method. Thus, the
appearance-based detection is used in the detection process. One of the
commonly used appearance-based detection approach is the HoG, which is
extracted by evaluating edge operators over the whole image and
discretizing and binning the orientations of the edge intensities into
histogram descriptors that are used for creating classification models.
HoG based approach is a commonly used in the appearance-feature-based
vehicle detection. HoG features are extracted by evaluating edge
operators over the whole image and discretizing and binning the
orientations of the edge intensities into histogram descriptors that are
used for creating classification models. Su et al. [11] proposed a
vehicle detection approach using HoG feature with the sliding window
method. The primary gradient direction has been calculated in order to
estimate the orientation of the vehicle. One weakness of the HoG is that
it is not rotational invariant feature, which is sensitive to the
direction of the targets. They tackle this problem by rotating the
sliding window to get the integral histogram values. Gleason et al. [12]
compared the performance of HoG feature and Histogram of Gabor
Coefficients (HGC) features used as the descriptors of vehicles, it
obtaining an average detection rate of 80%. According to the detection
rate figures the HoG has obtained better performance. They also applied
Harris corner detectors to identify the interest area of detection as
they assumed that vehicles usually contain a large number of edges and
corners. Point descriptor is also used in classification method apart
from HoG which acts as an area descriptor. Sahli et al. [13] proposed a
local feature-based approach based on Scale-Invariant Feature Transform
(SIFT) [5]. They used SIFT feature of vehicles and background to train a
SVM classifier to create a model that was used to classify vehicles and
background in query images. They obtained an accuracy of 95.2%.
Comparing the detection results between the HoG feature and SIFT feature
it apparently seems that SIFT feature is better. However, in terms of
real-time detection, SIFT feature needs to use more computational
resources especially when processing the whole image for small targets.
In this paper, the proposed approach integrated feature based method and
sliding window method by using HoG feature with a corner detection
algorithm FAST (Features from Accelerated Segment Test) which can
process quicker than the SIFT feature. Furthermore, the SIFT features
have been applied in the tracking section because of its high matching
accuracy and the long processing time problem has tackled by narrow the
search
2432015 Seventh International Conference of Soft Computing and
Pattern Recognition (SoCPaR 2015)area that the targets are most likely
to appear in the tracking process. https://codeshoppy.com/shop/product/on-road-vehicle-breakdown-assistance-app/
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