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Law enforcement Agencies (LEAs) are still using conventional, manpower based techniques to gather forensic evidence. Concealed surveillance devices can provide irrefutable evidences, but current video surveillance systems are usually bulky and complicated, are often used as simple video recorders, and require complex, expensive infrastructure to supply power, bandwidth, storage and illumination.
Recent years have seen significant advances in the surveillance industry, but these were rarely targeted to forensic applications. The imaging community is fixated on cameras for mobile phones, where the figures of merit are resolution, image quality, and low profile. A mobile phone with its camera on would consume its battery in under two hours. Industrial surveillance cameras are even more power hungry, while intelligent algorithms such as face detection often require extremely high processing power, such as backend server farms, and are not available in conventional surveillance systems.
The FORENSOR project (FOREnsic evidence gathering autonomous seNSOR), aims at developing an ultra-low-power, miniaturized, low-cost, wireless, autonomous sensor for evidence gathering, able to operate for up to two months without infrastructures.
- An example of image processing by the FORENSOR vision sensor
The video provides an example of the frame-by-frame processing embedded on the vision sensor developed in this project. The sensor captures a gray-level VGA image, sub-samples it to QQVGA and detects motion pixels according to a dynamic background subtraction algorithm. This latter basically compares the intensity of each pixel within two thresholds that model the background and are dynamically
updated. The motion map is then denoised by a morphological filter; the horizontal and vertical projections of the motion map are then computed, thresholded and binarized to remove further noise and finally used to define the condition for the alarm generation. Only the frames containing motion pixels are delivered to
the processor along with their corresponding motion maps.
The video shows the QQVGA input frame (top, left), the motion map before and after de-noising along with the horizontal and vertical projections (top, right), a spot indicating when the alarm is generated (bottom, left) and the data delivered to the processor (bottom, right).
- Motion detection performance: a qualitative analysis and comparison on some examples.
The videos (01 02 03 04 05) show a comparison of the proposed motion detection algorithm (FORENSOR) with a frame difference approach (FD) and a background subtraction approach (BS). All the algorithms take as input a VGA video (INPUT), subsample it to QQVGA and process it to detect the motion pixels. Precisely, FD computes the absolute difference D between pairs of subsequent frames, smooths D and de-noise the result by an erosion filter. The resulting map is binarized and scaled up to VGA. BS implements the technique presented in the paper Zivkovic, Zoran, and Ferdinand Van Der Heijden. "Efficient adaptive density estimation per image pixel for the task of background subtraction" Pattern Recognition Letters 27(7):773-780, 2006.
Here we use the OpenCV C++ code available at
As for FD, also for BS the resulting binary motion map is up-scaled back to VGA.
- Centre for Research and Technology Hellas (EL)
- JCP-Connect SAS (FR)
- STMicroelectronics (IT)
- Fondazione Bruno Kessler (IT)
- EMZA Visual Sensoe Ltd. (IL)
- Synelixis Solutions Ltd. (EL)
- Vrije Universiteit Brussel – Institute for European Sudies (BE)
- ALMAVIVA (IT)
- VISIONWARE (PT)
- Ayuntamiento de Valencia – Policia Local de Valencia (ES)
- Ministério da Justiça – Policia Judiciária Portugal (PT)
Massimo Gottardi (IRIS)Michela Lecca (TeV)
The contribution of TeV to the EU Project Forensor consists in to develop a software tool simulating the functionalities of the ultra-low power, surveillance sensor proposed by Forensor for crime fighting. This work aims at supporting and guiding the hardware implementation of the sensor and it is mainly carried on in collaboration with the Research Unit IRIS (https://iris.fbk.eu/) of CMM.