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Recognizing and Classification of Traffic Situations
Zbořil, Jiří ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to identify and classify dangerous situations from surveillance cameras, monitoring traffic. An example of such situations is dangerous standing near by the road and car crash, on which this work focuses. The created system uses object detector, analyzing average images in given interval, K nearest neighbor and K Means algorithm and re-detection of enlarged local area in a frame to select anomaly candidates. Detected objects, that do not belong on the road are eliminated by attaching created road mask. At the very last phase, the interval, together with the classification is determined. Calculated F1 score is 0.645, S4 score 0.535 and precision of classification 80 %.
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Recognizing and Classification of Traffic Situations
Zbořil, Jiří ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to identify and classify dangerous situations from surveillance cameras, monitoring traffic. An example of such situations is dangerous standing near by the road and car crash, on which this work focuses. The created system uses object detector, analyzing average images in given interval, K nearest neighbor and K Means algorithm and re-detection of enlarged local area in a frame to select anomaly candidates. Detected objects, that do not belong on the road are eliminated by attaching created road mask. At the very last phase, the interval, together with the classification is determined. Calculated F1 score is 0.645, S4 score 0.535 and precision of classification 80 %.
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