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Elixir: A System To Enhance Data Quality For Multiple Analytics On A Video Stream

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Elixir: A system to enhance data quality for multiple analytics on a video stream IoT sensors, especially video cameras, are ubiquitously deployed around the world to perform a variety of computer vision tasks in several verticals including retail, health- care, safety and security, transportation, manufacturing, etc. To amortize their high deployment effort and cost, it is desirable to perform multiple video analytics tasks, which we refer to as Analytical Units (AUs), off the video feed coming out of every camera. As AUs typically use deep learning-based AI/ML models, their performance depend on the quality of the input video, and recent work has shown that dynamically adjusting the camera setting exposed by popular network cameras can help improve the quality of the video feed and hence the AU accuracy, in a single AU setting. In this paper, we first show that in a multi-AU setting, changing the camera setting has disproportionate impact on different AUs performance. In particular, the optimal setting for one AU may severely degrade the performance for another AU, and further the impact on different AUs varies as the environmental condition changes. We then present Elixir, a system to enhance the video stream quality for multiple analytics on a video stream. Elixir leverages Multi-Objective Reinforcement Learning (MORL), where the RL agent caters to the objectives from different AUs and adjusts the camera setting to simultaneously enhance the performance of all AUs. To define the multiple objectives in MORL, we develop new AU-specific quality estimator values for each individual AU. We evaluate Elixir through real-world experiments on a testbed with three cameras deployed next to each other (overlooking a large enterprise parking lot) running Elixir and two baseline approaches, respectively. Elixir correctly detects 7.1% (22,068) and 5.0% (15,731) more cars, 94% (551) and 72% (478) more faces, and 670.4% (4975) and 158.6% (3507) more persons than the default-setting and time-sharing approaches, respectively. It also detects 115 license plates, far more than the time-sharing approach (7) and the default setting (0).

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APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning

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APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning Cameras are increasingly being deployed in cities, enterprises and roads world-wide to enable many applications in public safety, intelligent transportation, retail, healthcare and manufacturing. Often, after initial deployment of the cameras, the environmental conditions and the scenes around these cameras change, and our experiments show that these changes can adversely impact the accuracy of insights from video analytics. This is because the camera parameter settings, though optimal at deployment time, are not the best settings for good-quality video capture as the environmental conditions and scenes around a camera change during operation. Capturing poor-quality video adversely affects the accuracy of analytics. To mitigate the loss in accuracy of insights, we propose a novel, reinforcement-learning based system APT that dynamically, and remotely (over 5G networks), tunes the camera parameters, to ensure a high-quality video capture, which mitigates any loss in accuracy of video analytics. As a result, such tuning restores the accuracy of insights when environmental conditions or scene content change. APT uses reinforcement learning, with no-reference perceptual quality estimation as the reward function. We conducted extensive real-world experiments, where we simultaneously deployed two cameras side-by-side overlooking an enterprise parking lot (one camera only has manufacturer-suggested default setting, while the other camera is dynamically tuned by APT during operation). Our experiments demonstrated that due to dynamic tuning by APT, the analytics insights are consistently better at all times of the day: the accuracy of object detection video analytics application was improved on average by ∼ 42%. Since our reward function is independent of any analytics task, APT can be readily used for different video analytics tasks.

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CamTuner: Reinforcement Learning based System for Camera Parameter Tuning to enhance Analytics

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CamTuner: Reinforcement Learning based System for Camera Parameter Tuning to enhance Analytics Video analytics systems critically rely on video cameras, which capture high quality video frames, to achieve high analytics accuracy. Although modern video cameras often expose tens of configurable parameter settings that can be set by end users, deployment of surveillance cameras today often uses a fixed set of parameter settings because the end users lack the skill or understanding to reconfigure these parameters. In this paper, we first show that in a typical surveillance camera deployment, environmental condition changes can significantly affect the accuracy of analytics units such as person detection, face detection and face recognition, and how such adverse impact can be mitigated by dynamically adjusting camera settings. We then propose CAMTUNER, a framework that can be easily applied to an existing video analytics pipeline (VAP) to enable automatic and dynamic adaptation of complex camera settings to changing environmental conditions, and autonomously optimize the accuracy of analytics units (AUs) in the VAP. CAMTUNER is based on SARSA reinforcement learning (RL) and it incorporates two novel components: a light weight analytics quality estimator and a virtual camera. CAMTUNER is implemented in a system with AXIS surveillance cameras and several VAPs (with various AUs) that processed day long customer videos captured at airport entrances. Our evaluations show that CAMTUNER can adapt quickly to changing environments. We compared CAMTUNER with two alternative approaches where either static camera settings were used, or a strawman approach where camera settings were manually changed every hour (based on human perception of quality). We observed that for the face detection and person detection AUs, CAMTUNER is able to achieve up to 13.8% and 9.2% higher accuracy, respectively, compared to the best of the two approaches (average improvement of 8% for both AUs).

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