Adaptive Behaviour for a Self-Organising Video Surveillance System Using a Genetic Algorithm
<p>Schedule encoding scheme and evaluation of the input variables to the fitness function, for a two-cameras example (two rows) and a four steps periodic time-window (four columns).</p> "> Figure 2
<p>A typical environment semi-randomly generated for the numerical experiment setup. Colour as in <a href="#algorithms-14-00074-f001" class="html-fig">Figure 1</a>: the zoomed-in picture on the right shows overlap (orange area) if all cameras are turned on. Note the “blind spots” (grey area) that are permanently outside coverage.</p> "> Figure 3
<p>Benchmarking tests: <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> We show the evolution over time of variables <span class="html-italic">A</span>, <span class="html-italic">B</span> and <span class="html-italic">C</span> for the fittest individual of each generation (the plotted values are averaged over 20 independent realisations). If <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> then <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>,</mo> <mi>γ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; and <span class="html-italic">F</span> is equal to <span class="html-italic">A</span>. Consequently, the GA is optimising <span class="html-italic">F</span> by reducing the value of <span class="html-italic">A</span> to (eventually) zero. The convergence speed of the algorithm when using mutation only (<b>left</b> panel) is significantly slower than with sexual reproduction (<b>right</b> panel).</p> "> Figure 4
<p>Benchmarking tests: <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> </mrow> </semantics></math> i.e., the value of <span class="html-italic">F</span> is only based on blind spots. Both variations (<b>left</b>: mutation only; <b>right</b>: with sexual reproduction) perform comparable, the value of <span class="html-italic">B</span> may never drop entirely to zero due to unavoidable blind spots caused by the camera placement.</p> "> Figure 5
<p>Benchmarking tests: setting <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> implies (as above) that only one value contributes to <span class="html-italic">F</span>, namely, <span class="html-italic">C</span>, the <span class="html-italic">overlap</span>. In both variations of the GA <span class="html-italic">C</span> is being reduced but the GA with sexual reproduction (<b>right</b> panel) significantly outperforms the variation that uses only mutation (<b>left</b>).</p> "> Figure 6
<p>The curves show the evolution over time of the three global variables (for the values <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mi>γ</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mn>3</mn> </mfrac> </mrow> </semantics></math>) for the fittest individual of each generation (reported are the averaged values over 180 independent realisations).</p> "> Figure 7
<p>The curves show the evolution over time of the three global variables (for the values <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>γ</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> <mo>,</mo> <mi>γ</mi> <mo>=</mo> <mfrac> <mn>2</mn> <mn>4</mn> </mfrac> </mrow> </semantics></math>) for the fittest individual of each generation (the shown results are averaged over 180 independent realisations).</p> "> Figure 8
<p>The curves show the evolution over time of the three global variables (for the values <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mo>,</mo> <mi>γ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>) for the fittest individual of each generation (the presented results are the averages over 180 independent realisations).</p> "> Figure 9
<p>Response to a disruptive event (failure of 8 out of 64 cameras at generation 10,000). The curves show the evolution over time of the three global variables for the fittest individual of each generation (averaged over 180 independent realisations).</p> ">
Abstract
:1. Introduction
- The cameras must spent as little time as possible in the “active” state [2] in which they are consuming resources such as power, network bandwidth, disk storage, screen space on the monitoring workstation, etc.
- The fraction of the shopping mall surface that is not being monitored (i.e., “blind spots”) should be minimised.
- The overlap between “active” cameras (i.e., zones that are being shown on several concurrent video feeds) is wasteful and must also be minimised.
2. Schedule Encoding and Fitness Function
3. Genetic Algorithm, Reproduction, and Mutation/Selection Process
4. Numerical Experiment Set-Up
- No two cameras can be less than 4 m apart
- Cameras have at least 48 m from the monitored surface within their coverage area
5. Limit-Cases and Benchmarking
6. Exploration of the Weighting Parameters Space
6.1. Case: (Equal Weights)
6.2. Case: ,
6.3. Case: ,
7. Run-Time Adaptation and Real-World Considerations
8. Conclusions and Future Work
- There is room for further practical development on the implementation side, specifically with regard to the design of reliable techniques to acquire the information “consumed” by the genetic algorithm’s fitness function. For example, even though we do not think that this represents in any way an unsurmountable obstacle, measuring overlap between cameras could be a challenge in its own right. It is not hard to imagine empirical methods, such as target tracking in a pre-deployment training phase, to obtain a rough approximation (e.g., a binary matrix showing which pairs of cameras do and do not overlap), but making a more precise estimate (e.g., the surface of the overlapping coverage area) could prove difficult. However, as automated surveillance is a rapidly growing market, solutions to this challenge are being worked on. Furthermore, our numerical experiments indicate that this is the least critical of the three fitness variables, so it is certainly no “show stopper”.
- More exploration of the parameters space is required to quantify the influence of external parameters over the performance of the adaptive framework, and to determine the best internal parameter values for any given deployment scenario. The difficulty here comes from the large number of external parameters, some of which might not even have been identified yet, which, together with the very wide range of possible values for them, make a systematic approach unrealistic. We will focus our own efforts on scale (i.e., number and density of cameras) as we suspect they might have the strongest impact on the possible benefits of incorporating overlap into the fitness function. There are, however, many other aspects to document. In particular, it would be desirable to improve the way in which coverage is modelled, for instance, by taking into account distance from the camera and substituting a continuous variable (image quality) to our binary one.
Author Contributions
Funding
Conflicts of Interest
References
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Saffre, F.; Hildmann, H. Adaptive Behaviour for a Self-Organising Video Surveillance System Using a Genetic Algorithm. Algorithms 2021, 14, 74. https://doi.org/10.3390/a14030074
Saffre F, Hildmann H. Adaptive Behaviour for a Self-Organising Video Surveillance System Using a Genetic Algorithm. Algorithms. 2021; 14(3):74. https://doi.org/10.3390/a14030074
Chicago/Turabian StyleSaffre, Fabrice, and Hanno Hildmann. 2021. "Adaptive Behaviour for a Self-Organising Video Surveillance System Using a Genetic Algorithm" Algorithms 14, no. 3: 74. https://doi.org/10.3390/a14030074
APA StyleSaffre, F., & Hildmann, H. (2021). Adaptive Behaviour for a Self-Organising Video Surveillance System Using a Genetic Algorithm. Algorithms, 14(3), 74. https://doi.org/10.3390/a14030074