Efthimia Bilissi

Acceptable bit-rates for human face identification from CCTV imagery

The objective of this investigation is to produce recommendations for acceptable bit-rates of CCTV footage of people onboard London buses. The majority of CCTV recorders on buses use a proprietary format based on the H.264/AVC video coding standard, exploiting both spatial and temporal redundancy. Low bit-rates are favored in the CCTV industry but they compromise the image usefulness of the recorded imagery. In this context usefulness is defined by the presence of enough facial information remaining in the compressed image to allow a specialist to identify a person. The investigation includes four steps: 1) Collection of representative video footage. 2) The grouping of video scenes based on content attributes. 3) Psychophysical investigations to identify key scenes, which are most affected by compression. 4) Testing of recording systems using the key scenes and further psychophysical investigations. The results are highly dependent upon scene content. For example, very dark and very bright scenes were the most challenging to compress, requiring higher bit-rates to maintain useful information. The acceptable bit-rates are also found to be dependent upon the specific CCTV system used to compress the footage, presenting challenges in drawing conclusions about universal ‘average’ bit-rates.

Just noticeable gamma differences and acceptability of sRGB images displayed on a CRT monitor

Abstract The standard RGB colour space (sRGB) has been proposed as a means for obtaining accurate reproduction of colour and tone for images displayed across the Internet, provided that they are viewed under the reference display and viewing conditions defined in the standard. It has been found, however, that typical display and viewing conditions when accessing online images vary and therefore deviate from the reference sRGB conditions. One of the parameters that may affect the perceived quality of online images is the gamma setting of the display. In this work psychophysical experiments were conducted to determine the imperceptibility and acceptability of gamma differences of sRGB images when they are viewed on cathode ray tube displays. These experiments were carried out under both controlled and uncontrolled display and viewing conditions. The results of these experiments are presented and discussed, including the estimated points of subjective equality and the just noticeable difference of gamma values.

The effects of scene content parameters, compression, and frame rate on the performance of analytics systems

In this investigation we study the effects of compression and frame rate reduction on the performance of four video analytics (VA) systems utilizing a low complexity scenario, such as the Sterile Zone (SZ). Additionally, we identify the most influential scene parameters affecting the performance of these systems. The SZ scenario is a scene consisting of a fence, not to be trespassed, and an area with grass. The VA system needs to alarm when there is an intruder (attack) entering the scene. The work includes testing of the systems with uncompressed and compressed (using H.264/MPEG-4 AVC at 25 and 5 frames per second) footage, consisting of quantified scene parameters. The scene parameters include descriptions of scene contrast, camera to subject distance, and attack portrayal. Additional footage, including only distractions (no attacks) is also investigated. Results have shown that every system has performed differently for each compression/frame rate level, whilst overall, compression has not adversely affected the performance of the systems. Frame rate reduction has decreased performance and scene parameters have influenced the behavior of the systems differently. Most false alarms were triggered with a distraction clip, including abrupt shadows through the fence. Findings could contribute to the improvement of VA systems.

Comparative performance between human and automated face recognition systems, using CCTV imagery, different compression levels and scene parameters

In this investigation we identify relationships between human and automated face recognition systems with respect to compression. Further, we identify the most influential scene parameters on the performance of each recognition system. The work includes testing of the systems with compressed Closed-Circuit Television (CCTV) footage, consisting of quantified scene (footage) parameters. Parameters describe the content of scenes concerning camera to subject distance, facial angle, scene brightness, and spatio-temporal busyness. These parameters have been previously shown to affect the human visibility of useful facial information, but not much work has been carried out to assess the influence they have on automated recognition systems. In this investigation, the methodology previously employed in the human investigation is adopted, to assess performance of three different automated systems: Principal Component Analysis, Linear Discriminant Analysis, and Kernel Fisher Analysis. Results show that the automated systems are more tolerant to compression than humans. In automated systems, mixed brightness scenes were the most affected and low brightness scenes were the least affected by compression. In contrast for humans, low brightness scenes were the most affected and medium brightness scenes the least affected. Findings have the potential to broaden the methods used for testing imaging systems for security applications.

A case study in identifying acceptable bitrates for human face recognition tasks

Face recognition from images or video footage requires a certain level of recorded image quality. This paper derives acceptable bitrates (relating to levels of compression and consequently quality) of footage with human faces, using an industry implementation of the standard H.264/MPEG-4 AVC and the Closed-Circuit Television (CCTV) recording systems on London buses. The London buses application is utilized as a case study for setting up a methodology and implementing suitable data analysis for face recognition from recorded footage, which has been degraded by compression. The majority of CCTV recorders on buses use a proprietary format based on the H.264/MPEG-4 AVC video coding standard, exploiting both spatial and temporal redundancy. Low bitrates are favored in the CCTV industry for saving storage and transmission bandwidth, but they compromise the image usefulness of the recorded imagery. In this context, usefulness is determined by the presence of enough facial information remaining in the compressed image to allow a specialist to recognize a person. The investigation includes four steps: (1) Development of a video dataset representative of typical CCTV bus scenarios. (2) Selection and grouping of video scenes based on local (facial) and global (entire scene) content properties. (3) Psychophysical investigations to identify the key scenes, which are most affected by compression, using an industry implementation of H.264/MPEG-4 AVC. (4) Testing of CCTV recording systems on buses with the key scenes and further psychophysical investigations. The results showed a dependency upon scene content properties. Very dark scenes and scenes with high levels of spatial-temporal busyness were the most challenging to compress, requiring higher bitrates to maintain useful information. Identification of acceptable bitrates for human face recognition systems.The London buses application is the case study for the applied methodology.Results show the influence of scene content on acceptance thresholds.Dark scenes and scenes with high spatial-temporal busyness require lower compression.