Early Pedestrian intent Prediction Via Features Estimation

Motion compensated inter frame prediction is a common component of all video coders and greatly reduces temporal redundancy. With the rise of deep learning-based image and video compression, this concept has been successfully taken over from traditional coding approaches. These approaches offer a larger flexibility than traditional transform coding and therefore enable efficient conditional coding. in this work, we develop a novel conditional coding approach based on the generalized difference and generalized sum operators. This approach is a special case of a general conditional coder and has a very small complexity overhead. We also propose an extension which enables dynamic content-adaptive switching between conditional and residual coding. We show that the extended generalized difference coding outperforms both residual and conditional coding, saving 27.8% Bj?ntegaard delta rate compared to the former.