International Journal of Information Technology

, Volume 10, Issue 4, pp 417–434 | Cite as

A fine tuned tracking of vehicles under different video degradations

  • Mohamed Maher Ata
  • Mohamed El-Darieby
  • Mustafa Abd El-nabi
Original Research


Estimating the tracking efficacy of vehicles in traffic videos is one of the most desirable analysis specially in the presence of a challenging weather conditions. In this paper, a fine-tuning Kalman filter based tracking system has been proposed so that it will work robustly on the traffic videos. Such tracking efficacy has been tested and tuned by integrating a system that calculates the distance between adjacent vehicles as a case study. The integrated system could provide some sort of traffic warning system according to the allowable traffic safety standards. Analysis has been utilized in two phases; phase (1): by changing the performance indices of Kalman filter parameters (initial estimation error, motion noise, and measurement noise). We have measured both average number of assigned tracks and processing time of interest in order to acquire best tuning decision. From observations, changing values of initial estimation error has no effect on the performance of the tracking efficacy however increasing both motion noise and measurement noise has an adverse impact on the tracking performance. Phase (2) by applying the integrated system on a degraded version of a captured urban traffic video to measure performance of the tracking procedure according to the existence of salt and pepper, Gaussian, and Speckle video degradations. Such video disturbances could perform an evaluation for some sort of challenging weather conditions (e.g., rain, fog, and reduced light conditions). It is obviously that average number of assigned tracks has been degraded in the presence of video disturbance with respect to percentage of occurrence and the appropriate statistical features (mean, and variance) of such degradation. Twelve different types of filtering mask have been applied in order to measure average number of assigned tracks (correct predictions) after the cleaning process. We have measured the deviation between both the no noise and the with noise traffic video to study effect of each filter mask within each noise type of video disturbance. Such deviation measurements introduce a decision making criteria for best tuning that increases the efficacy of the vehicular tracking.


Intelligent transportation system (ITS) Blob analysis Kalman filter Assigned tracks Video degradation Salt and pepper Gaussian noise Speckle noise Filter mask 


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Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  • Mohamed Maher Ata
    • 1
  • Mohamed El-Darieby
    • 2
  • Mustafa Abd El-nabi
    • 1
  1. 1.Electrical Communication and Electronics Department, Faculty of EngineeringTanta UniversityTantaEgypt
  2. 2.Faculty of EngineeringRegina UniversityReginaCanada

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