As the tinyML community is acutely aware, adding Vision capability to a battery powered IoT device is non-trivial. The tremendous amount of vision data that needs to be processed necessitates the use of HW accelerators as well as clever algorithms that take advantage of data locality, sparsity and so on. A real world CV enabled IoT device requires attention to a range of other practical issues ranging from indoor/outdoor location, orientation, optics, sensor selection etc. This talk touches upon some of the practical considerations, tradeoffs and issues inherent in the design of a tinyCV system.

Embedded computer vision is nowadays adopted in several computing devices, consumer electronics and cyber-physical systems. Visual edge intelligence is a growing necessity for emerging applications where real-time decision is vital. Object detection, the first step in such applications, achieved tremendous improvements in terms of accuracy due to the emergence of Convolutional Neural Networks (CNNs) and Deep Learning. However, such complex paradigms require extensive resources, which prevents their deployment on resource-constraint mobile and embedded devices that simultaneously need to process high resolution images. Common approaches in reducing resources involve techniques such as quantization, pruning, compression, etc. While these techniques are efficient up to a certain aspect, they are built on traditional computationally inspired approaches. On the other hand, mammalian vision utilizes saliency and memory among other techniques, and limits attention during a visual search within a significantly limited search space. In this talk therefore, I will present our efforts to reduce the processing demands of edge-based CNN inference, via inclusion of 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, validated on UAV platforms in various applications involving car and pedestrian detection.