{'fraction_non_alphanumeric': 0.02389705882352941, 'fraction_numerical': 0.001838235294117647, 'mean_word_length': 5.083798882681564, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Traffic Surveillance Using Gabor Filter Bank and Kalman Predictor

This paper builds upon our earlier work by applying an optimized version of our non-linear scene prediction method to traffic surveillance video. As previously, a Gabor-filter bank has been selected as a primary detector for any changes in a given image sequence. The detected ROI (region of interest) in arbitrary motion is fed to a non-linear Kalman filter for predicting the next scene in time-varying video, which is subject to prediction error invalidation. Potential applications of this research are mainly in the areas of traffic control and monitoring, traffic flow surveillance, and MPEG video-compression. The reported experimental results show improved performance over the non-linear Kalman filtering based scene prediction results in our previous work. The low least mean square error (LMSE), on the average of about 2 to 3 % remains close to the average reported in our earlier work, however, the fluctuations in error have disappeared, proving the reliability of the approach to traffic-motion prediction.