ACIVS'12 Presentation by Francesco Flammini


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Cozzolino, A., Flammini, G., Galli, V., Lamberti, M., Poggi, G., Pragliola, C.: Evaluating the effects of MJPEG compression on Motion Tracking in metro railway surveillance. In: Proc. 14th Intl. Conf. on Advanced Concepts for Intelligent Vision Systems, ACIVS 2012, Sept. 4-7 2012, Brno, Czech Republic, J. Blanc-Talon et al. (Eds.), Springer LNCS 7517, pp. 142–154 (Springer-Verlag Berlin Heidelberg, Germany, ISBN 978-3-642-33139-8)

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ACIVS'12 Presentation by Francesco Flammini

  1. 1. ACIVS’12 Advanced Concepts for Intelligent Vision SystemsSept. 4-7 2012, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic Angelo Cozzolino, Francesco Flammini, Valentina Galli, Mariangela Lamberti, Giovanni Poggi, Concetta Pragliola Evaluating the effects of MJPEG compression on Motion Tracking in metro railway surveillance presented by Francesco Flammini Ansaldo STS – Innovation & Security Engineer
  2. 2. Video Content Analytics in transit systems • Transit systems attractive targets for: – Thieves – Vandals – Terrorists • Video surveillance essential for: – Deterrence – Detection – Response – Prosecution • VCA supports Safety & Security Surveillance, especially when there are: – High-number of cameras (hundreds to thousands) – Low number of operators • VS with VCA integrated in current PSIM (Physical Security Information Management) systems – Pros: superior situation awareness Ref. – Cons: possible issues with the number of false alarms Francesco Flammini: Critical Infrastructure Security: Assessment, Prevention, Detection, • Frequent requests of upgrade of legacy CCTV with modern VCA systems Response, 2011 (WIT Press, Southampton, UK, ISBN: 978-1-84564-562-5) • VCA event detection and performance requirements in recent tenders are increasingly demandingACIVS’12, Francesco Flammini 2
  3. 3. Performance evaluation of motion tracking • Ground Truth generation by annotation tools • GT includes for each frame: – Top-left – Bottom-right coordinates of the so called ‘bounding-boxes’ top surrounding objects detected in the scene left • MT metrics defined in the literature to measure the temporal and spatial overlap by comparison between the Ground Truth and Algorithm Result produced by the right bottom Motion Tracker, using appropriate thresholds False Negative False PositiveACIVS’12, Francesco Flammini 3
  4. 4. Evaluation method Video Metrics Video Selection GT Generation AR Generation Compression Computation• Videos have been analyzed by a Motion Tracker identical to the one installed in the real metro-railway but without using filters for alarm generation• The Motion Tracker has generated for each compression level an AR text file with detected objects, whose information was structured coherently with the ones included in the GTACIVS’12, Francesco Flammini 4
  5. 5. Video selection Concourse - 7 objects Platform – simulation of object left behind 4 CIF (720x576) 25 FPS Turnstiles – 7 objects 60s Tunnel portal – train passing, IR lamp ➩ 1500 framesACIVS’12, Francesco Flammini 5
  6. 6. MJPEG video compression C = 1 (Q = 100%) C ≈ 5 (Q = 50%) C ≈ 10 (Q = 20%) C ≈ 15 (Q = 10%) C ≈ 20 (Q = 5%) C ≈ 25 (Q =1%)ACIVS’12, Francesco Flammini 6
  7. 7. Metrics computation• For metrics evaluation, we have developed a Matlab program that automatically computes the FN and FP metrics. The tool organizes its input data (GT and AR) in appropriate arrays, whose number of rows is equal to the number of objects while the number of columns is 5, that is: – The list of frames in which the object is present (i.e. the track), that is a vector whose length is equal to the number of frames of the track – Top-left and bottom-right coordinates of the bounding-boxes (4 numbers)• It is being extended to compute other metrics (e.g. ‘ID change’)ACIVS’12, Francesco Flammini 7
  8. 8. Evaluation of results • Fluctuation of results due to algorithm adaptive thresholds depending on scene characteristics (e.g. objects size, ambient light, etc.) (a) (b) • ‘Filtering’ effect of the compression can counterbalance negative effect of quality degradation, by reducing the number of detectable objects (c) (d)ACIVS’12, Francesco Flammini 8
  9. 9. Evaluation of trends (a) (b)• As expected, tracking performance degrades generally with quality, and this has a much relevant impact at higher levels of compression, in particular when the image quality threshold is lower than 20%, that is at compression ratios higher than 10 (corresponding approximately to 4 Mbps bandwidth occupation)ACIVS’12, Francesco Flammini 9
  10. 10. Main causes of False Negatives • Tiling (right) and occlusions (down) prevent the tracker to ‘hook’ the objects in the scene, and thus to track (a) their trajectory, since their IDs change frequently as they were different objects (b) (c) (a) (b)ACIVS’12, Francesco Flammini 10
  11. 11. Main causes of False Positives Glare Reflections Camouflage Large artefactsACIVS’12, Francesco Flammini 11
  12. 12. Relevance of FP sources w.r.t. compression (a) (b) (c)• For the Concourse, all FP causes (especially glare) increase considerably with compression, while in Platform and Turnstiles the effects of artefacts is largely predominant with respect to other causes, which, however, continue to be relevant• Tunnel FP are not reported: since there is no real object moving in the scene, they show up only at train passage due to the light change in the scene; furthermore, the absence of most chromatic components w.r.t. other standard cameras (IR cameras only provide greyscale images) reduces the number of FP causes varying with compression levelsACIVS’12, Francesco Flammini 12
  13. 13. Conclusions and future developments• Performance degradation critical when passing from a 20% till a 1% quality level of compressed videos, whereas a 50% reduction on image quality represents a very acceptable trade-off (corresponding to ≈ 7 Mbps bandwidth occupation)• In all the cases in which it is required to go over that ‘conservative’ ratio, it is necessary to evaluate how the error sources are affected in the correct detection of the objects, according to the specific features of each scene (motion density, light sources, camera shots, type of background, etc.)• The results achieved can provide some guidelines which can be applicable in similar scenarios (technologies and contexts), e.g. using more efficient codecs• Using the same evaluation method in any domain it is possible to: – support the design of surveillance systems by fine-tuning the video compression level against scene characteristics or other factors, for each camera (especially useful in distributed wireless systems) – quantify the effect on VCA performance of other quality or noise factors like • sensitivity, resolution, frame rate, etc. • vibrations, electro-magnetic interference, chromatic distortions, etc.ACIVS’12, Francesco Flammini 13
  14. 14. Thank you for your kind attention Questions?