Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device

Currently, most designers face a daunting task to research different design flows and learn the intricacies of specific software from various manufacturers in hardware/software co-design. An urgent need of creating a scalable hardware/software co-design platform has become a key strategic element for developing hardware/software integrated systems. In this paper, we propose a new design flow for building a scalable co-design platform on FPGA-based system-on-chip. We employ an integrated approach to implement a histogram oriented gradients (HOG) and a support vector machine (SVM) classification on a programmable device for pedestrian tracking. Not only was hardware resource analysis reported, but the precision and success rates of pedestrian tracking on nine open access image data sets are also analysed. Finally, our proposed design flow can be used for any real-time image processingrelated products on programmable ZYNQ-based embedded systems, which benefits from a reduced design time and provide a scalable solution for embedded image processing products

Tree Memory Networks for Modelling Long-term Temporal Dependencies

In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question answering, part-of-speech tagging and machine translation. However this success in modelling short term dependencies has not successfully transitioned to application areas such as trajectory prediction, which require capturing both short term and long term relationships. In this paper, we propose a Tree Memory Network (TMN) for modelling long term and short term relationships in sequence-to-sequence mapping problems. The proposed network architecture is composed of an input module, controller and a memory module. In contrast to related literature, which models the memory as a sequence of historical states, we model the memory as a recursive tree structure. This structure more effectively captures temporal dependencies across both short term and long term sequences using its hierarchical structure. We demonstrate the effectiveness and flexibility of the proposed TMN in two practical problems, aircraft trajectory modelling and pedestrian trajectory modelling in a surveillance setting, and in both cases we outperform the current state-of-the-art. Furthermore, we perform an in depth analysis on the evolution of the memory module content over time and provide visual evidence on how the proposed TMN is able to map both long term and short term relationships efficiently via a hierarchical structure