MACHINE LEARNING
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Aiming to achieve near-human video understanding, in this project we analyze several gigabytes of spatiotemporal data to perform action recognition, multi-person tracking, object permanence and video reasoning. eigen has built a scalable video-understanding platform for long-form video reasoning that scales to new environments and camera angles without any re-training.
eigen also provides a system platform. Running both on the cloud (AWS) and on-prem, it can scale up to thousands of streams into it for cloud-based AI processing. Our AI video algorithms provide efficient streaming and inference. eigen has a web frontend and support for iOS/Android playback. eigen has been tested in several retail POCs serving 200+ streams; its behavioral analytics have also been evaluated through various NEC customers. Using mixed precision and TensorRT, eigen is extremely efficient and incurs very low cloud costs.
Publications
Collaborators: Hans Peter Graf, Farley Lai, Asim Kadav
Eigen Collaborators: Asim Kadav (Lead), Farley Lai, Deep Patel, Rowena Chen, Anupriya Prasad, Shayan Bhatti, Shrey Shah, Ramakrishnan Sundareswaran, Likitha Lakshminarayan, Aagam Shah, Maxwell Springer, Aaron Rosati
In this paper, we address the problem of inferring the layout of complex road scenes from video sequences. To this end, we formulate it as a top-view road attributes prediction problem and our goal is to predict these attributes for each frame both accurately and consistently. In contrast to prior work,
We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit the benefits of some readily accessible supervision signals from input data itself or some off-the-shelf functional models
