Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.

Abstract: The primary purpose of the present invention is to enable devices/machines/systems to perform an optimized video analytics on images and videos. The present invention focuses on detecting, tracking, and classifying objects in a scene. Here, the tracking is performed by comparing objects across at least two frames and then associating the objects based on a cost matrix. Some examples of the objects include, but are not limited to, persons, animals, vehicles, or any other articles or items.

Abstract: The present disclosure relate to effectively determining spatial and temporal features for extracting low-level and high-level features from image frames of a multimedia content. A plurality of image frames are received from an imaging unit. Spatial filters are applied on each image frame to generate a first set of activation maps which provide spatial features in the image frames. Further, a temporal filter is applied on the plurality of image frames at a plurality of levels to generate one or more second set of activation maps corresponding to each level for determining temporal features in the image frames. Thereafter, the spatial feature from each image and temporal feature of the plurality of image frames from each level is extracted, which represent low-level and high-level spatial and temporal features.