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Enhancing the Surveillance Detection Range of Image Sensors Using HDR Techniques

Image sensor-based surveillance systems are used for scene analysis, image sequence creation, and object detection. Natural scenes contain areas of high and low illumination, and standard cameras can only transform a fraction of the available visual information. The wide variation in illumination conditions limits the detection capabilities of image sensors. Therefore, in this study, the feasibility of applying high dynamic range (HDR) techniques using image sensors for enhancing range performance was investigated. The HDR requirements were analyzed, and available HDR techniques along with tone mapping and image enhancement were optimized for real-time scenarios. To determine the HDR requirements for specific visibility conditions, a theoretical analysis was conducted to evaluate camera performance using the human visual system. Additionally, a HDR imaging framework was established, and experiments were conducted to validate the performance of the framework. Results demonstrate that there is an empirical relationship between the dynamic range and visual detection range of the surveillance system. Subjective and objective evaluations indicate that surveillance cameras employing HDR imaging for long range applications have increased target detection range.