# Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
## 2 Related Work  
**Vehicle detection. ** Historically, background subtraction algorithms were often used for vehicle tracking [12; 13]. These methods work well in good weather conditions, and with a moderate traffic density. However, they fail in various illumination conditions (shadows, car lights in use at night), and in the case of high-density traffic. However, despite these disadvantages, these kinds of methods are commonly used, because they can often be deployed directly with no training nor calibration required. Vehicle detection falls into the category of object detection methods, which have recently been dominated by CNN-based methods. Recent comparative works on vehicle detection confirm this advantage [9; 14; 15]. One of the first algorithms that yielded impressive results was R-CNN [16]. For each image, around 2000 object-agnostic region proposals were computed while using Selective Search [17], and for each image, obtained CNN features were classified by support-vector-machine (SVM). Such a model for each region proposal (bounding box) returns a vector of class probabilities to which the given box belongs. Unfortunately, the performance was very low, with region definitions being the main bottleneck. Later region proposals were integrated into CNNs in an end-to-end manner in Faster-RCNN [18]. The demand for faster inference resulted in so-called one-stage detector networks, such as YOLO [19], SSD [20], and SqueezeDet [11]. Those models directly predict bounding box locations and class probabilities with a single network in a single attempt. The image is divided into \(W\)\({}^{*}H\) cells, and each cell is directly responsible for detecting objects. As a result, a much faster inference is achieved with a small loss in accuracy. It is only recently that deep learning has started to be used by intelligent transportation systems. Out of all of these algorithms, currently, only one-stage detectors are capable of obtaining real-time performance with limited computing power. However, most of the recent work in vehicle detection has focused on obtaining the best accuracy by using models utilising specialised GPUs [21; 22]. Therefore, one of the objectives of this work is to estimate what accuracy can be achieved on embedded systems, i.e., NVIDIA Jetson Tx2. That is why the efficient SqueezeDet architecture was used for vehicle detection. Details of that algorithm are recalled in the next section. **Vehicle tracking. ** Object tracking methods can be divided, in general, into online and offline methods. In the online case, only the current frame is available for the processing, while in offline methods, a sequence of frames is considered at once [23]. Having precomputed detections along the whole video sequence, different optimisation algorithms can be used for finding object trajectories, e.g., by energy minimisation [24]. However, in this work, we are interested, in on-line object tracking, as it is intended to be applied directly to practical traffic monitoring systems. [PERSON] et al. [25] used a CNN that was trained to regress directly from two images the location in the second image of the tracked object shown in the first image. Another recent work uses Siamese Networks for object tracking [26]. One of the advantages of such methods is that they can track an arbitrary object as long as the initial bounding box is given for that object. However, those works focus on single-object-tracking, and they require an initial bounding box that is not available in the real-world scenario of traffic monitoring. When compared to single-target-based, multi-object-tracking (MOT) is much more complicated due to the varying number of objects, and the interactions between these objects. Tracking-by-detection is a dominating paradigm in MOT. The algorithm is composed of three steps: detection, prediction, and association. First, the object detector is run on a target image. The prediction model is used for estimating all objects' new positions given their current states.