@inproceedings{JbailiThomanek2021,
  author    = {Khaled Jbaili and Jan Thomanek},
  title     = {Implementing and Evaluating a Tracking-By-Detection Algorithm for a Camera Monitoring System},
  series = {26. Interdisziplin{\"a}re Wissenschaftliche Konferenz Mittweida},
  number    = {002},
  publisher = {Hochschule Mittweida},
  address   = {Mittweida},
  issn      = {1437-7624},
  doi       = {10.48446/opus-12332},
  pages     = {193 -- 204},
  year      = {2021},
  abstract  = {In this paper, we designed, implemented, and tested a special surveillance camera system based on a combination of classical image processing algorithms. The system’s sub-objective consists of tracking experimental vehicles driving on a defined trajectories (Rail) in real time. Furthermore, it analyzes the scene to collect additional vehicles \& rail-related information. The system then uses the gathered data to reach its main objective which confines oneself in independently predicting vehicles collision. Consequently, we propose a hybrid method of detecting and tracking ATLAS-vehicles efficiently. To detect the vehicle at the beginning of the video, periodically every n-frame, and in the case where the tracked vehicle has been lost, we used Histogram Back-Projection. By contrast, Kernelized correlation filter is used to track the detected vehicles. Combining these two methods provides one of the best trade-offs between accuracy and speed even on a single processing core. The proposed method achieves the best performance compared with three different approaches on a custom dataset.},
  language  = {en}
}