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Current multi-person localisation and tracking systems have an over relianceon the use of appearance models for target re-identification and almost noapproaches employ a complete deep learning solution for both objectives. Wepresent a novel, complete deep learning framework for multi-person localisationand tracking. In this context we first introduce a light weight sequentialGenerative Adversarial Network architecture for person localisation, whichovercomes issues related to occlusions and noisy detections, typically found ina multi person environment. In the proposed tracking framework we build uponrecent advances in pedestrian trajectory prediction approaches and propose anovel data association scheme based on predicted trajectories. This removes theneed for computationally expensive person re-identification systems based onappearance features and generates human like trajectories with minimalfragmentation. The proposed method is evaluated on multiple public benchmarksincluding both static and dynamic cameras and is capable of generatingoutstanding performance, especially among other recently proposed deep neuralnetwork based approaches.

Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking