  Visual intelligence at the edge is becoming a growing necessity for low
latency applications and situations where real-time decision is vital. Object
detection, the first step in visual data analytics, has enjoyed significant
improvements in terms of state-of-the-art accuracy due to the emergence of
Convolutional Neural Networks (CNNs) and Deep Learning. However, such complex
paradigms intrude increasing computational demands and hence prevent their
deployment on resource-constrained devices. In this work, we propose a
hierarchical framework that enables to detect objects in high-resolution video
frames, and maintain the accuracy of state-of-the-art CNN-based object
detectors while outperforming existing works in terms of processing speed when
targeting a low-power embedded processor using an intelligent data reduction
mechanism. Moreover, a use-case for pedestrian detection from
Unmanned-Areal-Vehicle (UAV) is presented showing the impact that the proposed
approach has on sensitivity, average processing time and power consumption when
is implemented on different platforms. Using the proposed selection process our
framework manages to reduce the processed data by 100x leading to under 4W
power consumption on different edge devices.
