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We present a novel adaptive online learning (AOL) framework to predict humanmovement trajectories in dynamic video scenes. Our framework learns and adaptsto changes in the scene environment and generates best network weights fordifferent scenarios. The framework can be applied to prediction models andimprove their performance as it dynamically adjusts when it encounters changesin the scene and can apply the best training weights for predicting the nextlocations. We demonstrate this by integrating our framework with two existingprediction models: LSTM [3] and Future Person Location (FPL) [1]. Furthermore,we analyze the number of network weights for optimal performance and show thatwe can achieve real-time with a fixed number of networks using the leastrecently used (LRU) strategy for maintaining the most recently trained networkweights. With extensive experiments, we show that our framework increasesprediction accuracies of LSTM and FPL by ~17% and 28% on average, and up to~50% for FPL on the worst case while achieving real-time (20fps).

AOL: Adaptive Online Learning for Human Trajectory Prediction in Dynamic Video Scenes