# Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
## 2 Related Work
Finally, the association model is applied for associating the currently tracked objects with new detections. In recent work, vehicle motion is modelled with group behaviour [27]. In the approach, named SORT [28], Kalman filtering is used for the prediction step, and the Hungarian algorithm is implemented for the association between the existing tracked vehicles and new detections. It is a very efficient and accurate method with the only drawback being the lack of handling long-term occlusions. This was later fixed by utilising recent advances in Deep Learning to learn the association metric [29]. Unfortunately, this approach requires training and also more computation power engagement. Another approach uses computing Wasserstein distance as association metric allowing for obtaining very good vehicle tracking performance, however is also computationally expensive [30]. In more recent work, [PERSON] et al. used a very straightforward method working without any prediction model [31]. It works on the assumption that dense frames are available for tracking. In the real world, this assumption does not always hold, especially when performing tracking on embedded systems, where only a few frames per second are available. This is why, in this work, the SORT tracker was used. **Domain Adaptation. ** Appearance changes based on lighting, weather conditions, and also because different class distributions of objects provide a significant challenge for systems relying on machine learning models for perception. Trained models are biased towards the datasets they were trained on, and the model performance drops in the test domain when the conditions are different. In particular, it is impossible in many situations to collect and annotate a representative dataset. To overcome this issue, many methods that fall into the category of domain adaptation have been proposed. Sometimes it is assumed that a limited amount of annotated data is available in the target domain [32], however such a scenario is not always feasible, which is why we focus on unsupervised adaptation. One of the approaches assumes learning a transformation that maps feature representations from the target domain to the source domain [33]. A popular technique is a self-training method, which involves creating an initial baseline model on fully labelled data, and then exploiting this model to estimate labels on a novel unlabelled dataset. The obtained pseudo-labels are further used for model fine-tuning. Such a method was successful, for example, in the semantic segmentation domain [34; 35]. One of the challenges in this technique is the fact that pseudo-labels can be inaccurate. However, because a video stream is analysed, the temporal coherency, and motion modelling can provide additional cues that can improve adaptation. One of the first approaches was revealed in the work of [PERSON] et al. [36], where labels were automatically found in the target domain using object tracking, and they were later used for model fine-tuning. Recent work models the data in multiple feature spaces to further reduce the number of false positives using the decorrelated error technique [10]. We are inspired by these works, and employ tracking and background modelling to assure efficiency in automatically obtained labels. The fine-tuning of the model on automatically labelled data only might result in catastrophic forgetting of previously learned information [37]. The rehearsal technique, in which, during the fine-tuning, automatically labelled data are mixed with the data that the model was initially trained with, has proven to be effective in minimising such an effect [38]; hence, this technique is used in our work. Moreover, adding a minimal amount of human effort might be beneficial for the machine learning system [39]. Consequently, experiments with such a human-in-the-loop scenario were performed, and inaccurate automatically obtained labels were manually filtered out.