Suchergebnisse

Synthetic simulated environments are gaining popularity in the Deep Learning Era, as they can alleviate the effort and cost of two critical tasks to build multi-camera systems for surveillance applications: setting up the camera system to cover the use cases and generating the labeled dataset to train the required Deep Neural Networks (DNNs). However, there are no simulated environments ready to solve them for all kind of scenarios and use cases. Typically, 'ad hoc' environments are built, which cannot be easily applied to other contexts. In this work we present a methodology to build synthetic simulated environments with sufficient generality to be usable in different contexts, with little effort. Our methodology tackles the challenges of the appropriate parameterization of scene configurations, the strategies to generate randomly a wide and balanced range of situations of interest for training DNNs with synthetic data, and the quick image capturing from virtual cameras considering the rendering bottlenecks. We show a practical implementation example for the detection of incorrectly placed luggage in aircraft cabins, including the qualitative and quantitative analysis of the data generation process and its influence in a DNN training, and the required modifications to adapt it to other surveillance contexts. ; This work has received funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under grant agreement No. 865162, SmaCS (https://www.smacs.eu/)

Currently, aircraft cabin operations such as the verification of taxi, take-off, and landing (TTL) cabin readiness are done manually. This results in an increased workload for the crew, operational inefficiencies, and a non-negligible risk of human errors in handling safety-related procedures. For TTL, specific cabin readiness requirements apply to the passenger, to the position of seat components and cabin luggage. The usage of cameras and vision-based object-recognition algorithms may offer a promising solution for specific functionalities such as cabin luggage detection. However, building a suitable camera-based smart sensing system for this purpose brings many challenges as it needs to be low weight, with competitive cost and robust recognition capabilities on individual seat level, complying with stringent constraints related to airworthiness certification. This position paper analyzes and discusses the main technological factors that system designers should consider for building such an intelligent system. These include the sensor setup, system training, the selection of appropriate camera sensors and lenses, AI-processors, and software tools for optimal image acquisition and image content analysis with Deep Neural Network (DNN)-based recognition methods. Preliminary tests with pre-trained generalist DNN-based object detection models are also analyzed to assist with the training and deployment of the recognition methods.