Abstract-This paper presents an alternative Forward Error Correction scheme, based on Reed-Solomon codes, with the aim of protecting the transmission of RTP-multimedia streams: the inter-packet symbol approach. This scheme is based on an alternative bit structure that allocates each symbol of the Reed-Solomon code in several RTP-media packets. This characteristic permits to exploit better the recovery capability of Reed-Solomon codes against bursty packet losses.The performance of our approach has been studied in terms of encoding/decoding time versus recovery capability, and compared with other proposed schemes in the literature. The theoretical analysis has shown that our approach allows the use of a lower size of the Galois Fields compared to other solutions. This lower size results in a decrease of the required encoding/decoding time while keeping a comparable recovery capability. Finally, experimental results have been carried out to assess the performance of our approach compared to other schemes in a simulated environment, where models for wireless and wireline channels have been considered.

This paper proposes an enhanced forward error correction (FEC) scheme based on small block low-density parity-check (LDPC) codes to protect real-time packetized multimedia streams in bursty channels. The use of LDPC codes is typically addressed for channels where losses are uniformly distributed (memoryless channels) and for large information blocks. This work suggests the use of this type of FEC codes at the application layer, in bursty channels (e.g., Internet protocol (IP)-based networks) and for real-time scenarios that require low transmission latency. To fulfil these constraints, the appropriate configuration parameters of an LDPC scheme have been determined using small blocks of information and adapting the FEC code to be capable of recovering packet losses in bursty environments. This purpose is achieved in two steps. The first step is performed by an algorithm that estimates the recovery capability of a given LDPC code in a burst packet loss network. The second step is the optimization of the code: an algorithm optimizes the parity matrix structure in terms of recovery capability against the specific behavior of the channel with memory. Experimental results have been obtained in a simulated transmission channel to show that the optimized LDPC matrices generate a more robust protection scheme against bursty packet losses for small information blocks.

In this paper we present a FEC scheme based on simple LDGM codes to protect packetized multimedia streams. We demonstrate that simple LDGM codes working with a limited number of packets (small values of k) obtain recovery capabilities, against bursty packet losses, that are similar to those of other more complex FEC-based schemes designed for this type of channels.

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.

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