On Road Vehicle Breakdown Assistance Finder Project – Code Shoppy

On Road Vehicle Breakdown Assistance Finder Project


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.

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.