Automatic video-object segmentation employing multi-sprites with constrained delay

This paper proposes an automatic video-object segmentation system for consumer media, based on the background subtraction technique. In order to allow for camera motion, the input images are aligned to a large panoramic background-sprite image. A multi-sprite technique is applied to minimize the size of the synthesized sprite images. In contrast to previous algorithms, which required multiple passes over the input data, the proposed algorithm enables online processing with a fixed processing delay. This is important for implementation in consumer devices which have to run in real-time with constrained resources. We provide example results illustrating that a real-time segmentation on memory-constrained hardware is feasible

Automatic human face detection for home surveillance application

This paper concentrates on exploiting human face informa tion for surveillance applications in a home environment. The system features real-time human face detection and facial feature identification. It is our aim to insert the results in a video-security system architec ture, where MPEG-4 coding techniques enable low bit-rate video trans mission over a home network environment. The processing contains the following essential elements: (1) skin-color segmentation, (2) histogram analysis for facial feature detection, and (3) probability-based confidence value evaluation of facial features. We have tested our sequence of pro cessing algorithms on a set of video sequences. The experimental results show that our approach offers near real-time processing speed with good detection capability, but the robustness needs further improvement

System requirements and considerations for visual table of contents in PVR

With the introduction of non-tape-based digital video recorders also known as personal video recorders (PVR), consumers can expect alternative navigation methods to the well-known trick-play modes found on analog and digital tape-based systems. In this paper, we explore the system requirements and aspects of a visual table of contents (VTOC). A primary advantage of implementing a VTOC search mode is that it provides a much higher visual performance at high search speeds (50 times or higher) than the conventional search techniques. We present a solution for generating a video signal for visual search that is based on reusing MPEG-2 compressed video data. The video search signal is composed of a set of MPEG-2 compressed subpictures, resulting in a mosaic screen. An efficient strategy is introduced, that allows either full or partial reuse of the compressed sub-pictures via motion compensation of earlier reference sub-pictures to allow the generation of a new mosaic screen

Multidimensional model of estimated resource usage for multimedia NoC QoS

Multiprocessor systems are rapidly entering various high-performance computing segments, like multimedia processing. Instead of an increase in processor clock frequency, the new trend is enabling multiple cores in performing processing, e.g. dual or quadrapule CPUs in one subsystem. In this contribution, we address the problem of modeling the resource requirements of multimedia applications for a distributed computation on a multiprocessor system. This paper shows that the estimation of resource requirements based on input data enables the dynamic activation of tasks and run-time redistribution of application tasks. We also formally specify the optimal selection of the co-executed application with aim to provide the most optimal end-results of such streaming applications within one networks-on-chip (NoC) system. We present a new concept for system optimization which involves the major system parameters and resource usage. Experimental results are based on mapping an arbitrary-shaped MPEG-4 video decoder onto a multiprocessor NoC

Cascaded face detection using neural network ensembles

We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost

Enabling arbitrary rotation camera-motion using multi-sprites with minimum coding cost

Object-oriented coding in the MPEG-4 standard enables the separate processing of foreground objects and the scene background (sprite). Since the background sprite only has to be sent once, transmission bandwidth can be saved.We have found that the counter-intuitive approach of splitting the background into several independent parts can reduce the overall amount of data. Furthermore, we show that in the general case, the synthesis of a single background sprite is even impossible and that the scene background must be sent as multiple sprites instead. For this reason, we propose an algorithm that provides an optimal partitioning of a video sequence into independent background sprites (a multisprite), resulting in a significant reduction of the involved coding cost. Additionally, our sprite-generation algorithm ensures that the sprite resolution is kept high enough to preserve all details of the input sequence, which is a problem especially during camera zoom-in operations. Even though our sprite generation algorithm creates multiple sprites instead of only a single background sprite, it is fully compatible with the existing MPEG-4 standard. The algorithm has been evaluated with several test sequences, including the well-known Table-tennis and Stefan sequences. The total coding cost for the sprite VOP is reduced by a factor of about 2.6 or even higher, depending on the sequence

Identity verification using computer vision for automatic garage door opening

We present a novel system for automatic identification of vehicles as part of an intelligent access control system for a garage entrance. Using a camera in the door, cars are detected and matched to the database of authenticated cars. Once a car is detected, License Plate Recognition (LPR) is applied using character detection and recognition. The found license plate number is matched with the database of authenticated plates. If the car is allowed access, the door will open automatically. The recognition of both cars and characters (LPR) is performed using state-ofthe- art shape descriptors and a linear classifier. Experiments have revealed that 90% of all cars are correctly authenticated from a single image only. Analysis of the computational complexity shows that an embedded implementation allows user authentication within approximately 300ms, which is well within the application constraints