Giovanni Gualdi

Video Streaming for Mobile Video Surveillance

Mobile video surveillance represents a new paradigm that encompasses, on the one side, ubiquitous video acquisition and, on the other side, ubiquitous video processing and viewing, addressing both computer-based and human-based surveillance. To this aim, systems must provide efficient video streaming with low latency and low frame skipping, even over limited bandwidth networks. This work presents MoSES (MObile Streaming for vidEo Surveillance), an effective system for mobile video surveillance for both PC and PDA clients; it relies over H.264/AVC video coding and GPRS/EDGE-GPRS network. Adaptive control algorithms are employed to achieve the best tradeoff between low latency and good video fluidity. MoSES provides a good-quality video streaming that is used as input to computer-based video surveillance applications for people segmentation and tracking. In this paper new and general-purpose methodologies for streaming performance evaluation are also proposed and used to compare MoSES with existing solutions in terms of different parameters (latency, image quality, video fluidity, and frame losses), as well as in terms of performance in people segmentation and tracking.

Multistage Particle Windows for Fast and Accurate Object Detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.

Multi-stage sampling with boosting cascades for pedestrian detection in images and videos

Many works address the problem of object detection by means of machine learning with boosted classifiers. They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to the classifier. Several methods have been proposed to speed up the search (adding complementary features or using specialized hardware). In this paper we propose a statistical-based search approach for object detection which uses a Monte Carlo sampling approach for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multi-stage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifier (i.e. its response). For videos, this approach is plugged in a Bayesian-recursive framework which exploits the temporal coherency of the pedestrians. Several tests on both still images and videos on common datasets are provided in order to demonstrate the relevant speedup and the increased localization accuracy with respect to sliding window strategy using a pedestrian classifier based on covariance descriptors and a cascade of Logitboost classifiers.

Low-Latency Live Video Streaming over Low-Capacity Networks

This paper presents an effective system for streaming over low-capacity networks (such as GPRS and EGPRS) of live videos with low latency. Existing solutions are either too complex or not suitable to our scope. For this reason, we developed a complete, ready-to-use streaming system based on H.264/AVC codec and UDP/IP stack. The system employs adaptive controls to achieve the best tradeoff between low latency and good video fluency, by keeping the UDP buffer occupancy at the decoder side between two given levels. Our experiments demonstrate that this system is able to transmit live videos at CIF format and 10 fps over GPRS/EGPRS with very low latency (1.73 sec on average, basically due to the network delay), good fluency and average quality, measured with PSNR, of 31 dB on GPRS at 23 kbps at 10 fps

Contextual information and covariance descriptors for people surveillance: an application for safety of construction workers

In computer science, contextual information can be used both to reduce computations and to increase accuracy. This paper discusses how it can be exploited for people surveillance in very cluttered environments in terms of perspective (i.e., weak scene calibration) and appearance of the objects of interest (i.e., relevance feedback on the training of a classifier). These techniques are applied to a pedestrian detector that uses a LogitBoost classifier, appropriately modified to work with covariance descriptors which lie on Riemannian manifolds. On each detected pedestrian, a similar classifier is employed to obtain a precise localization of the head. Two novelties on the algorithms are proposed in this case: polar image transformations to better exploit the circular feature of the head appearance and multispectral image derivatives that catch not only luminance but also chrominance variations. The complete approach has been tested on the surveillance of a construction site to detect workers that do not wear the hard hat: in such scenarios, the complexity and dynamics are very high, making pedestrian detection a real challenge.

Mobile video surveillance systems: an architectural overview

The term mobile is now added to most of computer based systems as synonymous of several different concepts, ranging on ubiquitousness, wireless connection, portability, and so on. In a similar manner, also the name mobile video surveillance is spreading, even though it is often misinterpreted with just limited views of it, such as front-end mobile monitoring, wireless video streaming, moving cameras, distributed systems. This chapter presents an overview of mobile video surveillance systems, focusing in particular on architectural aspects (sensors, functional units and sink modules). A short survey of the state of the art is presented. The chapter will also tackle some problems of video streaming and video tracking specifically designed and optimized for mobile video surveillance systems, giving an idea of the best results that can be achieved in these two foundation layers.

Covariance descriptors on moving regions for human detection in very complex outdoor scenes

The detection of humans in very complex scenes can be very challenging, due to the performance degradation of classical motion detection and tracking approaches. An alternative approach is the detection of human-like patterns over the whole image. The present paper follows this line by extending Tuzel et al.s technique [1] based on covariance descriptors and LogitBoost algorithm applied over Riemannian manifolds. Our proposal represents a significant extension of it by: (a) exploiting motion information to focus the attention over areas where motion is present or was present in the recent past; (b) enriching the human classifier by additional, dedicated cascades trained on positive and negative samples taken from the specific scene; (c) using a rough estimation of the scene perspective, to reduce false detections and improve system performance. This approach is suitable in multi-camera scenarios, since the monolithic block for human-detection remains the same for the whole system, whereas the parameter tuning and set-up of the three proposed extensions (the only camera-dependent parts of the system), are automatically computed for each camera. The approach has been tested on a construction working site where complexity and dynamics are very high, making human detection a real challenge. The experimental results demonstrate the improvements achieved by the proposed approach.

Enabling technologies on hybrid camera networks for behavioral analysis of unattended indoor environments and their surroundings

This paper presents a layered network architecture and the enabling technologies for accomplishing vision-based behavioral analysis of unattended environments. Specifically the vision network covers both the attended environment and its surroundings by means of hybrid cameras. The layer overlooking at the surroundings is laid outdoor and tracks people, monitoring entrance/exit points. It recovers the geometry of the site under surveillance and communicates people positions to a higher level layer. The layer monitoring the unattended environment undertakes similar goals, with the addition of maintaining a global mosaic of the observed scene for further understanding. Moreover, it merges information coming from sensors beyond the vision to deepen the understanding or increase the reliability of the system. The behavioral analysis is demanded to a third layer that merges the information received from the two other layers and infers knowledge about what happened, happens and will be likely happening in the environment. The paper also describes a case study that was implemented in the Engineering Campus of the University of Modena and Reggio Emilia, where our surveillance system has been deployed in a computer laboratory which was often unaccessible due to lack of attendance.

A multi-stage pedestrian detection using monolithic classifiers

Despite the many efforts in finding effective feature sets or accurate classifiers for people detection, few works have addressed ways for reducing the computational burden introduced by the sliding window paradigm. This paper proposes a multi-stage procedure for refining the search for pedestrians using the HOG features and the monolithic SVM classifier. The multi-stage procedure is based on particle-based estimation of pdfs and exploits the margin provided by the classifier to draw more particles on the areas where the classifiers response is higher. This iterative algorithm achieves the same accuracy than sliding window using less particles (and thus being more efficient) and, conversely, is more accurate when configured to work at the same computational load. Experimental results on publicly available datasets demonstrate that this method, previously proposed for boosted classifiers only, can be successfully applied to monolithic classifiers.

PEANO: pictorial enriched annotation of video

In this DEMO, we present a tool set for video digital library management that allows i) structural annotation of edited videos in MPEG-7 by automatically extracting shots and clips; ii) automatic semantic annotation based on perceptual similarity against a taxonomy enriched with pictorial concepts iii) video clip access and hierarchical summarization with stand-alone and web interface iv) access to clips from mobile platform in GPRS-UMTS video-streaming. The tools can be applied in different domain-specific Video Digital Libraries. The main novelty is the possibility to enrich the annotation with pictorial concepts that are added to a textual taxonomy in order to make the automatic annotation process more fast and often effective. The resulting multimedia ontology is described in the MPEG-7 framework. The PEANO (Perceptual Annotation of Video) tool has been tested over video art , sport (Soccer, Olimpic Games 2006, Formula 1) and news clips.