ORUSSI: Optimal Road sUrveillance based on Scalable vIdeoERA–SME 2009 (6th call)

Bertini M., Del Bimbo A., Dini F., Nunziati W., Seidenari L.

Magenta s.r.l., Florence, Italy,Media Integration and Communication Center, University of Florence, ItalyIntegrasys inc., Madrid, Spain

{fabrizio.dini,walter.nunziati}@magentalab.it - http://www.magentalab.it{bertini,delbimbo,seidenari}@dsi.unifi.it - http://www.micc.unifi.it

Project Goals

The growing mobility of people and goods has a very high societal cost in terms of traffic congestion and of fatalities and injured people every year. The managementof a road network needs efficient ways for assessment at minimal costs. Road monitoring is a relevant part of road management, especially for safety, optimal trafficflow and for investigating new sustainable transport patterns. On the road side, there are several technologies used for collecting detection and surveillance information:sophisticated automated systems such as in-roadway or over-roadway sensors, closed circuit television (CCTV) system for viewing real-time video images of the roadwayor road weather information systems for monitoring pavement and weather. Current monitoring systems based on video lack of optimal usage of networks and aredifficult to be extended efficiently. Our project focuses on road monitoring through a network of roadside sensors (mainly cameras) that can be dynamically deployedand added to the surveillance systems in an efficient way. The main objective of the project is to develop an optimized platform offering innovative real-time media(video and data) applications for road monitoring in real scenarios. The project will develop a novel platform based on the synergetic bundling of current researchresults in the field of semantic transcoding, the recently approved standard Scalable Video Coding standard (SVC), wireless communication and roadside equipment.

On-board Video Analysis

Demo 1: Vehicle counting and speed estimation on AxisADP platformWe developed an embedded traffic monitoring application on the Axis ADPplatform. The application runs directly on camera and provides vehicle countingand statistics on the velocity of observed vehicles. In a nutshell:

I Virtual sensors extract a signal from the pixels’ value, based on edges intensity;

I the signal is used to infer the presence of a vehicle onto the sensor, making itpossible to realize vehicle counting;

I by using two properly arranged sensor, speed estimation is possible;

I allows very efficient implementation: only the pixels corresponding to thesensors area have to be processed;

I robust to a wide range of lighting and evironmental conditions.

Demo 2: Fast Feature DetectionWe show how it is possible to extract low-level features directly on the camera. Low-level features are the starting point of several possible image processing applications.Our application* extracts FAST corners that can be used for:

I Camera calibration.

I (PTZ) Camera tracking.

I Text localization.

I Image complexitycharacterization.

I Camera tamperingdetection

* we thank Leonardo Galteri who participated in the development of the on-board application.

Off-line Video Analysis

Demo 3: Anomaly DetectionTo capture scene dynamic statistics together with appearance in video surveillanceapplication, we propose a method [1] based on dense spatio-temporal features.These features are exploited in a real-time anomaly detection system. Anomalydetection is performed using a non-parametric modelling, evaluating directly localdescriptor statistics, and an unsupervised or semi-supervised approach. A methodto update scene statistics, to cope with scene changes that typically happen inreal world settings, is also provided. The proposed method is tested on publiclyavailable datasets and compared to other state-of-the-art approaches.

Demo 4: Selective TranscodingOur approach [2] is based on adaptive smoothing of individual video frames so thatimage features highly correlated to semantically interesting objects are preserved.This adaptive smoothing can be seamlessly inserted into a video coding pipeline as apre-processing state. Experiments show that our technique is efficient, outperformsstandard H.264 encoding at comparable bitrates, and preserves features critical fordownstream detection and recognition.

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Dataset

Dataset for vehicle counting and classification: Thanks to the involvement of Comune di Prato (a local municipality), we were able to collect a verywide dataset that turned out to be key for the project activities. The dataset is made of more than 250 hours of recording taken on a well-travelled county road, withdifferent lighting and weather conditions. From these video sequences we have extracted an image dataset of about 1250 vehicle images. The data set, publiclyavailable at www.micc.unifi.it/projects/orussi, will be used to train a vehicle classifier.

References[1] M. Bertini, A. Del Bimbo, L. Seidenari, “Dense Spatio-temporal Features For Non-parametric Anomaly Detection And Localization” in Proc. of ARTEMIS Int‘l Workshop on Analysis and Retrieval of Tracked Events and Motion in

Imagery Streams, Florence, Italy, 2010

[2] A.D. Bagdanov, M. Bertini, A. Del Bimbo, L. Seidenari, “Adaptive Video Compression for Video Surveillance Applications” in Proc. of ISM Int‘l Symposium on Multimedia, Dana Point, California, USA, 2011.