Weighed query-specific distance and hybrid NARX neural network for video object retrieval

The technical revolution in the field of video recording using the surveillance videos has increased the amount of the video databases that caused the need for an efficient video management system. This paper proposes a hybrid model using the nearest search algorithm (NSA) and the Levenberg–Marquardt (LM)-based non-linear autoregressive exogenous (NARX) neural network for performing the video object retrieval using the trajectories. Initially, the position of the objects in the video are retrieved using NSA and NARX individually, and they are averaged to determine the position of the object. The positions determined using the hybrid model is compared with the original database, and the trajectories of the objects are retrieved based on the minimum distance, which depends on the weighed query-specific distance. Experiments have been carried out using seven videos taken from the CAVIAR dataset, and the performance of the proposed method is compared with the existing methods. This proposed method found to be better than the existing method with respect to multiple object tracking precision (MOTP), multiple object tracking accuracy (MOTA), average tracking accuracy (ATA), precision, recall and F-measure that results a greater MOTP rate of 0.8796, precision rate of 0.8154, recall rate of 0.8408, the F-measure at a rate of 0.8371, MOTA of 0.8459 and ATA of 0.8324.