Marleen Morbée

Rate Allocation Algorithm for Pixel-Domain Distributed Video Coding Without Feedback Channel

In some video coding applications, it is desirable to reduce the complexity of the video encoder at the expense of a more complex decoder. Distributed video (DV) coding is a new paradigm that aims to achieve this. To allocate a proper number of bits to each frame, most DV coding algorithms use a feedback channel (FBC). However, in some cases, a FBC does not exist. In this paper, we therefore propose a rate allocation (RA) algorithm for pixel-domain distributed video coders without FBC. Our algorithm estimates at the encoder the number of bits for every frame without significantly increasing the encoder complexity. Experimental results show that our RA algorithm delivers satisfactory estimates of the adequate encoding rate, especially for sequences with little motion.

Principal view determination for camera selection in distributed smart camera networks

Within a camera network, the contribution of a camera to the observation of a scene depends on its viewpoint and on the scene configuration. This is a dynamic property, as the scene content is subject to change over time and the camera configuration might not be fixed, e.g. in a mobile network. In this work, we address the problem of effectively determining the principle viewpoint within a network, i.e. the view that contributes most to the desired observation of a scene. This selection is based on the information from each camerapsilas observations of persons in a scene, and only low data rate information is required to be sent over wireless channels since the image frames are first locally processed by each sensor node before transmission. The principal view, complemented with one or more helper views, constitutes a significantly more efficient scene representation than the totality of the available views. This is of great value for the reduction of the amount of image data that needs to be stored or transmitted over the network.

Camera selection for tracking in distributed smart camera networks

Tracking persons with multiple cameras with overlapping fields of view instead of with one camera leads to more robust decisions. However, operating multiple cameras instead of one requires more processing power and communication bandwidth, which are limited resources in practical networks. When the fields of view of different cameras overlap, not all cameras are equally needed for localizing a tracking target. When only a selected set of cameras do processing and transmit data to track the target, a substantial saving of resources is achieved. The recent introduction of smart cameras with on-board image processing and communication hardware makes such a distributed implementation of tracking feasible. We present a novel framework for selecting cameras to track people in a distributed smart camera network that is based on generalized information-theory. By quantifying the contribution of one or more cameras to the tracking task, the limited network resources can be allocated appropriately, such that the best possible tracking performance is achieved. With the proposed method, we dynamically assign a subset of all available cameras to each target and track it in difficult circumstances of occlusions and limited fields of view with the same accuracy as when using all cameras.

Sub-optimal Camera Selection in Practical Vision Networks through Shape Approximation

Within a camera network, the contribution of a camera to the observations of a scene depends on its viewpoint and on the scene configuration. This is a dynamic property, as the scene content is subject to change over time. An automatic selection of a subset of cameras that significantly contributes to the desired observation of a scene can be of great value for the reduction of the amount of transmitted and stored image data. We propose a greedy algorithm for camera selection in practical vision networks where the selection decision has to be taken in real time. The selection criterion is based on the information from each camera sensors observations of persons in a scene, and only low data rate information is required to be sent over wireless channels since the image frames are first locally processed by each sensor node before transmission. Experimental results show that the performance of the proposed greedy algorithm is close to the performance of the optimal selection algorithm. In addition, we propose communication protocols for such camera networks, and through experiments, we show the proposed protocols improve latency and observation frequency without deteriorating the performance.

Optimal camera selection in vision networks for shape approximation

Within a camera network, the contribution of a camera to the observation of a scene depends on its viewpoint and on the scene configuration. This is a dynamic property, as the scene content is subject to change over time. An automatic selection of a subset of cameras that significantly contributes to the desired observation of a scene can be of great value for the reduction of the amount of transmitted or stored image data. In this work, we propose low data rate schemes to select from a vision network a subset of cameras that provides a good frontal observation of the persons in the scene and allows for the best approximation of their 3D shape. We also investigate to what degree low data rates trade off quality of reconstructed 3D shapes.

Efficient approximate foreground detection for low-resource devices

A broad range of very powerful foreground detection methods exist because this is an essential step in many computer vision algorithms. However, because of memory and computational constraints, simple static background subtraction is very often the technique that is used in practice on a platform with limited resources such as a smart camera. In this paper we propose to apply more powerful techniques on a reduced scan line version of the captured image to construct an approximation of the actual foreground without overburdening the smart camera. We show that the performance of static background subtraction quickly drops outside of a controlled laboratory environment, and that this is not the case for the proposed method because of its ability to update its background model. Furthermore we provide a comparison with foreground detection on a subsampled version of the captured image. We show that with the proposed foreground approximation higher true positive rates can be achieved.

Reduced decoder complexity and latency in pixel-domain Wyner-Ziv video coders

In some video coding applications, it is desirable to reduce the complexity of the video encoder at the expense of a more complex decoder. Wyner–Ziv (WZ) video coding is a new paradigm that aims to achieve this. To allocate a proper number of bits to each frame, most WZ video coding algorithms use a feedback channel, which allows the decoder to request additional bits when needed. However, due to these multiple bit requests, the complexity and the latency of WZ video decoders increase massively. To overcome these problems, in this paper we propose a rate allocation (RA) algorithm for pixel-domain WZ video coders. This algorithm estimates at the encoder the number of bits needed for the decoding of every frame while still keeping the encoder complexity low. Experimental results show that, by using our RA algorithm, the number of bit requests over the feedback channel—and hence, the decoder complexity and the latency—are significantly reduced. Meanwhile, a very near-to-optimal rate-distortion performance is maintained.

A Distributed Coding-Based Extension of a Mono-View to a Multi-View Video System

Multi-view video systems provide 3D information about the captured scene. This 3D information can be useful for many emerging applications, e.g. 3D TV or virtual reality. However, many current video systems consist only of one camera and consequently do not capture the 3D content of a scene. In this paper, we therefore present an efficient, flexible and low-complexity method for extending an existing mono video system to a 3D system. The main idea is to develop a coding framework that starts from a single camera and that can be flexibly extended by low-complexity cameras to capture 3D video data. These cameras do not perform any motion or disparity estimation, but still good coding efficiency is achieved by relying on distributed video (DV) coding principles, i.e. jointly decoding of the independently encoded frames of the multi-view cameras. If we compare our coding results with the results for low-complexity DV coding of a single video, then higher efficiency is achieved since not only the motion between the frames of the video but also disparity between different views of the array of cameras is exploited at the decoder.

Scalable feature-based video retrieval for mobile devices

We present a novel method for video retrieval on mobile devices. The target scenario is the following: features are extracted from a captured query video at the user side and transmitted to a remote server; then, video retrieval is applied within a stored database at the server side and relevant information about the query is returned to the user. In particular, we focus on the user side and propose a scalable method for video feature extraction and encoding taking into consideration the processing capabilities of the device and available bandwidth. Despite these constraints, the first results show a promising accuracy of retrieval.

Generalized PCM Coding of Images

Pulse-code modulation (PCM) with embedded quantization allows the rate of the PCM bitstream to be reduced by simply removing a fixed number of least significant bits from each codeword. Although this source coding technique is extremely simple, it has poor coding efficiency. In this paper, we present a generalized PCM (GPCM) algorithm for images that simply removes bits from each codeword. In contrast to PCM, however, the number and the specific bits that a GPCM encoder removes in each codeword depends on its position in the bitstream and the statistics of the image. Since GPCM allows the encoding to be performed with different degrees of computational complexity, it can adapt to the computational resources that are available in each application. Experimental results show that GPCM outperforms PCM with a gain that depends on the rate, the computational complexity of the encoding, and the degree of inter-pixel correlation of the image.