Robust Baggage Detection and Classification Based on Local Tri-directional Pattern

In recent decades, the automatic video surveillance system has gained significant importance in computer vision community. The crucial objective of surveillance is monitoring and security in public places. In the traditional Local Binary Pattern, the feature description is somehow inaccurate, and the feature size is large enough. Therefore, to overcome these shortcomings, our research proposed a detection algorithm for a human with or without carrying baggage. The Local tri-directional pattern descriptor is exhibited to extract features of different human body parts including head, trunk, and limbs. Then with the help of support vector machine, extracted features are trained and evaluated. Experimental results on INRIA and MSMT17 V1 datasets show that LtriDP outperforms several state-of-the-art feature descriptors and validate its effectiveness.

Systimator: A Design Space Exploration Methodology for Systolic Array based CNNs Acceleration on the FPGA-based Edge Nodes

The evolution of IoT based smart applications demand porting of artificial intelligence algorithms to the edge computing devices. CNNs form a large part of these AI algorithms. Systolic array based CNN acceleration is being widely advocated due its ability to allow scalable architectures. However, CNNs are inherently memory and compute intensive algorithms, and hence pose significant challenges to be implemented on the resource-constrained edge computing devices. Memory-constrained low-cost FPGA based devices form a substantial fraction of these edge computing devices. Thus, when porting to such edge-computing devices, the designer is left unguided as to how to select a suitable systolic array configuration that could fit in the available hardware resources. In this paper we propose Systimator, a design space exploration based methodology that provides a set of design points that can be mapped within the memory bounds of the target FPGA device. The methodology is based upon an analytical model that is formulated to estimate the required resources for systolic arrays, assuming multiple data reuse patterns. The methodology further provides the performance estimates for each of the candidate design points. We show that Systimator provides an in-depth analysis of resource-requirement of systolic array based CNNs. We provide our resource estimation results for porting of convolutional layers of TINY YOLO, a CNN based object detector, on a Xilinx ARTIX 7 FPGA.