2D recurrent neural networks: a high-performance tool for robust visual tracking in dynamic scenes

Abstract

This paper proposes a novel method for robust visual tracking of arbitrary objects, based on the combination of image-based prediction and position refinement by weighted correlation. The effectiveness of the proposed approach is demonstrated on a challenging set of dynamic video sequences, extracted from the final of triple jump at the London 2012 Summer Olympics. A comparison is made against five baseline tracking systems. The novel system shows remarkable superior performances with respect to the other methods, in all considered cases characterized by changing background, and a large variety of articulated motions. The novel architecture, from here onwards named 2D Recurrent Neural Network (2D-RNN), is derived from the well-known Recurrent Neural Network model and adopts nearest neighborhood connections between the input and context layers in order to store the temporal information content of the video. Starting from the selection of the object of interest in the first frame, neural computation is applied to predict the position of the target in each video frame. Normalized cross-correlation is then applied to refine the predicted target position. 2D-RNN ensures limited complexity, great adaptability and a very fast learning time. At the same time, it shows on the considered dataset fast execution times and very good accuracy, making this approach an excellent candidate for automated analysis of complex video streams.

[Retracted] Mean Shift Fusion Color Histogram Algorithm for Nonrigid Complex Target Tracking in Sports Video

“…e study of moving shadow detection techniques by Andres Sanin who used shadow removal as a key step to improve object detection and tracking and trackability as an unbiased method to determine the actual use of shadow detection methods summarized the different performances of all shadow detection methods. Masala proposed a method to extract unique invariant features from images method [12]. is method can be used to perform reliable matching between different views of an object or scene.…”