Parallelization Of Objectoriented Machine Vision Algorithms For Embedded GPUs Decision logic based on machine vision is among the prevalent building blocks of todayu0027s smart devices, vehicles and things. A vast community in both industry and academia works to design and improve machine vision algorithms. Design phases often disregard end device architecture and specifics, relying on pseudocode, scripting or object oriented approaches. Objectoriented code increases the complexity of porting and optimization for end multiprocessor systems. Parallelization becomes required to reach realtime performance, with GPUs being among the most frequently utilized accelerators. In this paper, we propose methods and steps to efficiently parallelize machine vision code designed in objectoriented paradigm. We address challenges such as call stack usage optimization, code compacting, object dereferencing and partitioning for efficient execution on GPU kernels. Finally, we evaluate the proposed method by porting and optimizing an automotive camerabased vehicle detection algorithm.

Development Environment Proposal for Automotive VisionBased Algorithms Next generation autonomous driving functions heavily depend on various computationally intensive algorithms based on machine vision. Design and development of such algorithms is challenging, requiring engagement of a wider community and the availability of appropriate, costeffective tooling. Setting up a hardware and software environment that would both reflect actual automotive requirements and facilitate development and prototyping, is an uneasy task. In this paper, we analyze the available development platforms which could be utilized in automotive machine vision, with specific regard to new algorithm requirements, such as the utilization of AI, hardware acceleration and low latency. We give a proposal of an affordable and scalable development setup consisting of heterogeneous, visionenabled SoCs and automotive interfaces.