{'fraction_non_alphanumeric': 0.02297872340425532, 'fraction_numerical': 0.000851063829787234, 'mean_word_length': 5.157068062827225, '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}}
Use of multiple visual features for object tracking

In this paper we present a method of using multiple visual attributes (features) that are present in moving objects for carrying out object tracking, by way of feature substitution. The proposed method, in principle, can make use of many visual cues available from a scene such as texture, color, velocity (monocular features) and disparity, vergence (binocular features). For the present experiments we make use of 3 features, namely, texture, optical flow and color as the main visual features and defocus of objects (blur) as supportive feature. At any instance, tracking is carried out using only one feature and this feature is monitored closely for failures. The feature is substituted with another suitable feature only upon the failure or high uncertainty of the current features. In case of tracking using texture alone, we make use of a histogram based technique called Histogram Intersection Value. This technique is not only computationally simple but provides very good results whenever texture is suitable for tracking. Experimental results with real image sequences show the validity of the proposed method.