SUNRISE TECHNOLOGY INC — Department of Energy SBIR Phase I: 06b

Description

Today’s smart devices, such as collision avoidance system, rely on detectionof past and current events and then react to mitigate and reduce the severity of the ultimate consequence-The lack of proactiveness often renders such a detection system less useful for emergency situation- The ability to predict the future based on what has been heard, sensed, observed at the current moment is ofutmost importance for real-time systems, such as autonomous driving, safety and security monitoring and warning- It is quite common that a prediction system completely reconstructs (i-e-, predicts) the raw image pixels of the future frame-Howeverit does not necessarily reflect human intelligence where the future is in an abstract format and complete visual reconstruction of a scene is overkill for decision making and tooslow for smart devices, such as autonomous driving, collision avoidance system, and safety monitor- A novel approach, i-e-, Deep Sensor, is introduced in this proposal to execute learning tasks in a contextually-meaningful latent space, generate the abstract representations (embeddings) of future frames, and then perform classification among the learned sequence of embeddings- The objectives in this proposal are to integrate recent successes in neural networks, smart sensors and the Internet of Things (IoTs) and design a cost-effective real-time prediction system that learns from the sequenceof past video frames and generates the abstract representation of frames for up to several seconds in future-