A high‐performance face detection system using OpenMP

Hadjidoukas, Panagiotis E.; Dimakopoulos, Vassilios V.; Delakis, M.; García, Christophe

doi:10.1002/cpe.1389

Cited by 16 publications

(12 citation statements)

References 8 publications

(7 reference statements)

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“…As already mentioned in Section 2, we also experimented with a full face detection application, which has been described in detail by Hadjidoukas et al [5]. It takes as input an image and discovers the number of faces depicted in it, along with their position in the image.…”

Section: Face Detectionmentioning

confidence: 99%

Task-Based Execution of Nested OpenMP Loops

Agathos

Hadjidoukas

Dimakopoulos

2012

OpenMP in a Heterogeneous World

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Abstract. In this work we propose a novel technique to reduce the overheads related to nested parallel loops in OpenMP programs. In particular we show that in many cases it is possible to replace the code of a nested parallel-for loop with equivalent code that creates tasks instead of threads, thereby limiting parallelism levels while allowing more opportunities for runtime load balancing. In addition we present the details of an implementation of this technique that is able to perform the whole procedure completely transparently. We have experimented extensively to determine the effectiveness of our methods. The results show the actual performance gains we obtain (up to 25% in a particular application) as compared to other OpenMP implementations that are forced to suffer nested parallelism overheads.

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Section: Face Detectionmentioning

confidence: 99%

Task-Based Execution of Nested OpenMP Loops

Agathos

Hadjidoukas

Dimakopoulos

2012

OpenMP in a Heterogeneous World

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“…Furthermore, we propose efficient parallelisation strategies based on two different architectures: (i) message passing and (ii) shared memory architecture. OpenMP API is used, which allows incremental and scalable parallelisation of existing code, providing thus a fast way of parallelising the face detection system with minimal changes to its data structures and algorithm [13].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Face Detection and Tracking Utilising OpenMP and ROS

Tusa

Akbarinia

Rodríguez

et al. 2015

2015 Asia-Pacific Conference on Computer Aided System Engineering

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The first requisite of a robot to succeed in social interactions is accurate human localisation, i.e. subject detection and tracking. Later, it is estimated whether an interaction partner seeks attention, for example by interpreting the position and orientation of the body. In computer vision, these cues usually are obtained in colour images, whose qualities are degraded in ill illuminated social scenes. In these scenarios depth sensors offer a richer representation. Therefore, it is important to combine colour and depth information. The second aspect that plays a fundamental role in the acceptance of social robots is their real-time-ability. Processing colour and depth images is computationally demanding. To overcome this we propose a parallelisation strategy of face detection and tracking based on two different architectures: message passing and shared memory. Our results demonstrate high accuracy in low computational time, processing nine times more number of frames in a parallel implementation. This provides a real-time social robot interaction.

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“…In this context, the most successful algorithms for face detection are usually executed on real‐time systems on high‐end CPUs in order to leverage their high processing power . However, similar implementations executed on low‐power CPUs (eg, those present on mobile devices) will not work fast enough to meet real‐time restrictions.…”

Section: Introductionmentioning

confidence: 99%

Acceleration and energy consumption optimization in cascading classifiers for face detection on low‐cost ARM big. LITTLE asymmetric architectures

Corpas

Costero

Botella

et al. 2018

Circuit Theory & Apps

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This paper proposes a mechanism to accelerate and optimize the energy consumption of a face detection software based on Haar-like cascading classifiers, taking advantage of the features of low-cost asymmetric multicore processors (AMPs) with limited power budget. A modelling and task scheduling/allocation is proposed in order to efficiently make use of the existing features on big. LITTLE ARM processors, including (1) source-code adaptation for parallel computing, which enables code acceleration by applying the OmpSs programming model, a task-based programming model that handles data-dependencies between tasks in a transparent fashion; (2) different OmpSs task allocation policies which take into account the processor asymmetry and can dynamically set processing resources in a more efficient way based on their particular features.The proposed mechanism can be efficiently applied to take advantage of the processing elements existing on low-cost and low-energy multi-core embedded devices executing object detection algorithms based on cascading classifiers.Although these classifiers yield the best results for detection algorithms in the field of computer vision, their high computational requirements prevent them from being used on these devices under real-time requirements. Finally, we compare the energy efficiency of a heterogeneous architecture based on AMPs with a suitable task scheduling with that of a homogeneous symmetric architecture.

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A high‐performance face detection system using OpenMP

Cited by 16 publications

References 8 publications

Task-Based Execution of Nested OpenMP Loops

Task-Based Execution of Nested OpenMP Loops

Real-Time Face Detection and Tracking Utilising OpenMP and ROS

Acceleration and energy consumption optimization in cascading classifiers for face detection on low‐cost ARM big. LITTLE asymmetric architectures

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