An Embedded Vision Engine (EVE) for automotive vision processing

Mandal, Dipan Kumar; Sankaran, Jagadeesh; Gupta, Akshay; Castille, Kyle; Gondkar, Shraddha; Kamath, Sanmati; Sundar, Pooja; Phipps, A.

doi:10.1109/iscas.2014.6865062

Cited by 10 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These processors are typically designed to be power-efficient by avoiding floating-point and having a simplified processing pipeline. For instance, performance/watt of TI's Embedded Vision Engine (EVE) [9] is ∼8X than that of A15. On the other hand, it lacks flexibility and it is typically used for the pixel-level initial stages of the pipeline.…”

Section: Socmentioning

confidence: 99%

Computer vision in automated parking systems: Design, implementation and challenges

Heimberger

Horgan

Hughes

et al. 2017

Image and Vision Computing

View full text Add to dashboard Cite

Automated driving is an active area of research in both industry and academia. Automated Parking, which is automated driving in a restricted scenario of parking with low speed manoeuvring, is a key enabling product for fully autonomous driving systems. It is also an important milestone from the perspective of a higher end system built from the previous generation driver assistance systems comprising of collision warning, pedestrian detection, etc. In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms. Designing a low-cost system with functional safety is challenging and leads to a large gap between the prototype and the end product, in order to handle all the corner cases. We demonstrate how camera systems are crucial for addressing a range of automated parking use cases and also, to add robustness to systems based on active distance measuring sensors, such as ultrasonics and radar. The key vision modules which realize the parking use cases are 3D reconstruction, parking slot marking recognition, freespace and vehicle/pedestrian detection. We detail the important parking use cases and demonstrate how to combine the vision modules to form a robust parking system. To the best of the authors' knowledge, this is the first detailed discussion of a systemic view of a commercial automated parking system.

show abstract

Section: Socmentioning

confidence: 99%

Computer vision in automated parking systems: Design, implementation and challenges

Heimberger

Horgan

Hughes

et al. 2017

Image and Vision Computing

View full text Add to dashboard Cite

show abstract

“…Embedded systems such as wearable devices, drones, robots, and tablets are supposed to support CV applications [3]. Domains that employ CV include surveillance [4,5], gesture recognition [6], face tracking [7,8], medical imaging [9,10], automotive safety [11,12], and food industry [13][14][15], among others.…”

Section: Introductionmentioning

confidence: 99%

Exploiting architectural features of a computer vision platform towards reducing memory stalls

Mustafa

O’Riordan

Rogers

et al. 2018

J Real-Time Image Proc

View full text Add to dashboard Cite

Computer vision applications are becoming more and more popular in embedded systems such as drones, robots, tablets, and mobile devices. These applications are both compute and memory intensive, with memory bound stalls (MBS) making a significant part of their execution time. For maximum reduction in memory stalls, compilers need to consider architectural details of a platform and utilize its hardware components efficiently. In this paper, we propose a compiler optimization for a vision-processing system through classification of memory references to reduce MBS. As the proposed optimization is based on the architectural features of a specific platform, i.e., Myriad 2, it can only be applied to other platforms having similar architectural features. The optimization consists of two steps: affinity analysis and affinity-aware instruction scheduling. We suggest two different approaches for affinity analysis, i.e., source code annotation and automated analysis. We use LLVM compiler infrastructure for implementation of the proposed optimization. Application of annotation-based approach on a memory-intensive program shows a reduction in stall cycles by 67.44%, leading to 25.61% improvement in execution time. We use 11 different image-processing benchmarks for evaluation of automated analysis approach. Experimental results show that classification of memory references reduces stall cycles, on average, by 69.83%. As all benchmarks are both compute and memory intensive, we achieve improvement in execution time by up to 30%, with a modest average of 5.79%.

show abstract

“…Embedded vision applications are currently ingrained into many aspects of modern life, from automotive safety (Mandal et al, 2014), optical character recognition (Neumann and Matas, 2012), and gesture interfaces (Rautaray and Agrawal, 2015) to medical instrumentation (Economou and Papaioannou, 2013) and surveillance systems (Lin et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

OpenVX-Based Python Framework for Real-time Cross-Platform Acceleration of Embedded Computer Vision Applications

Heimlich

Tsur

2016

Front. ICT

View full text Add to dashboard Cite

Embedded real-time vision applications are being rapidly deployed in a large realm of consumer electronics, ranging from automotive safety to surveillance systems. However, the relatively limited computational power of embedded platforms is considered as a bottleneck for many vision applications, necessitating optimization. OpenVX is a standardized interface, released in late 2014, in an attempt to provide both system and kernel level optimization to vision applications. With OpenVX, Vision processing is modeled with coarse-grained data flow graphs, which can be optimized and accelerated by the platform implementer. Current full implementations of OpenVX are given in the programming language C, which neither supports advanced programming paradigms, such as object-oriented, imperative, and functional programming, nor does it has runtime or type checking. Here, we present a python-based full Implementation of OpenVX, which eliminates much of the discrepancies between the object-oriented paradigm used by many modern applications and the native C implementations. Our open-source implementation can be used for rapid development of OpenVX applications in embedded platforms. Demonstration includes static and real-time image acquisition and processing using a Raspberry Pi and a GoPro camera. Code is given in Supplementary Material. Code project and linked deployable virtual machine are located on GitHub: https://github.com/ NBEL-lab/PythonOpenVX.

show abstract

An Embedded Vision Engine (EVE) for automotive vision processing

Cited by 10 publications

References 4 publications

Computer vision in automated parking systems: Design, implementation and challenges

Computer vision in automated parking systems: Design, implementation and challenges

Exploiting architectural features of a computer vision platform towards reducing memory stalls

OpenVX-Based Python Framework for Real-time Cross-Platform Acceleration of Embedded Computer Vision Applications

Contact Info

Product

Resources

About