False positive reduction of location specific event classification

A system for training an on-site filter classifier, comprising:

• a memory storing a code;
• and
• at least one processor configured to execute the code, the code comprising:
• code instructors to receive a plurality of on-site feature vectors created by at least one classifier deployed in a certain site to classify at least one predefined anomaly event based on imagery data captured in the certain site, the at least one classifier is trained using a plurality of non-site-specific samples;
• code instructors to select a plurality of false positive feature vectors extracted from a subset of the plurality of on-site feature vectors having a score exceeding a certain threshold;
• code instructors to receive a plurality of true positive feature vectors extracted from positive samples reflecting the at least one predefined anomaly event;
• code instructors to train at least one site-specific filter classifier using the plurality of false positive feature vectors and the plurality of true positive feature vectors;
• and
• code instructors to provide the at least one site-specific filter classifier for filtering out at least one false positive detection of the at least one anomaly event detected in the certain site by the at least one classifier.

A method of reducing false positive classification events using site-specific filter classifiers, comprising:

• using at least one processor for:
• receiving at least one on-site sample classified as positive by at least one classifier deployed in a certain site to detect at least one predefined anomaly event based on imagery data depicting at least part of the certain site;
• applying at least one site-specific filter classifier to compute a filter classification score for an on-site feature vector extracted from the at least one on-site sample;
• estimating whether the at least one on-site sample is true positive or false positive based on comparison of the filter classification score to a filter threshold;
• filtering out the at least one on-site sample in case the filter classification score does not exceed the filter threshold;
• and
• outputting at least one detection indication in case the filter classification score exceeds the filter threshold indicative of detection of the at least one predefined anomaly event;
• wherein the at least one classifier is trained using a plurality of non-site-specific samples;
• wherein the at least one site-specific filter classifier is trained using a plurality of false positive feature vectors and a plurality of true positive feature vectors, the plurality of false positive feature vectors are extracted from a subset of a plurality of on-site samples having a score, computed by the at least one classifier, exceeding a certain threshold, and the plurality of true positive feature vectors extracted from a subset of the plurality of non-site-specific samples reflecting the at least one predefined anomaly event.