An Efficient Hardware Implementation of CNN-Based Object Trackers for Real-Time Applications Article Swipe

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Al‐Hussein A. El‐Shafie , Mohamed I. Zaki , S. E. D. Habib ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.36227/techrxiv.14445267

The object tracking research continues to be active since long period because of the several real-world variations imposed in the tracking process, like occlusion, changing appearance, illumination changes and cluttered background. With wide range of applications, embedded implementations are typically pursed for the tracking systems. Although object trackers based on Convolution Neural Network (CNN) have achieved state-of-the-art performance, they challenge the embedded implementations because of slow speed and large memory requirement. In this paper, we address these limitations on the algorithm-side and the circuitside. On the algorithm side, we adopt interpolation schemes which can significantly reduce the processing time and the memory storage requirements. We also evaluate the approximation of the hardware-expensive computations aiming for an efficient hardware implementation. Moreover, we modified the online-training scheme in order to achieve a constant processing time across all video frames. On the circuit side, we developed a hardware accelerator of the online training stage. We avoid the transposed reading from the external memory to speed-up the data movement with no performance degradation. Our proposed hardware accelerator achieves 45.9 frames-per-second in training the fully connected layers.

Related Topics

Computer Science

Convolutional Neural Network

Computer Hardware

Process (Computing)

Hardware Acceleration

Artificial Intelligence

Parallel Computing

Field-Programmable Gate Array

Algorithm

Eye Tracking

Pedagogy

Concepts

Computer science BitTorrent tracker Interpolation (computer graphics) Convolutional neural network Computer hardware Process (computing) Video tracking Hardware acceleration Computation Tracking (education) Speedup Object (grammar) Artificial intelligence Parallel computing Field-programmable gate array Algorithm Eye tracking Image (mathematics) Psychology Pedagogy Operating system

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Type: preprint
Language: en
Landing Page: https://doi.org/10.36227/techrxiv.14445267
PDF: https://www.techrxiv.org/articles/preprint/An_Efficient_Hardware_Implementation_of_CNN-Based_Object_Trackers_for_Real-Time_Applications/14445267/1/files/27629160.pdf
OA Status: gold
References: 27
Related Works: 10
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DOI: https://doi.org/10.36227/techrxiv.14445267

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Title: An Efficient Hardware Implementation of CNN-Based Object Trackers for Real-Time Applications

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-04-21

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Authors: Al‐Hussein A. El‐Shafie, Mohamed I. Zaki, S. E. D. Habib

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Landing page: https://doi.org/10.36227/techrxiv.14445267

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PDF URL: https://www.techrxiv.org/articles/preprint/An_Efficient_Hardware_Implementation_of_CNN-Based_Object_Trackers_for_Real-Time_Applications/14445267/1/files/27629160.pdf

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Concepts: Computer science, BitTorrent tracker, Interpolation (computer graphics), Convolutional neural network, Computer hardware, Process (computing), Video tracking, Hardware acceleration, Computation, Tracking (education), Speedup, Object (grammar), Artificial intelligence, Parallel computing, Field-programmable gate array, Algorithm, Eye tracking, Image (mathematics), Psychology, Pedagogy, Operating system

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References (count): 27

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Related works (count): 10

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abstract_inverted_index.constant	130
abstract_inverted_index.embedded	36, 61
abstract_inverted_index.evaluate	106
abstract_inverted_index.external	158
abstract_inverted_index.hardware	117, 144, 171
abstract_inverted_index.modified	121
abstract_inverted_index.movement	164
abstract_inverted_index.process,	21
abstract_inverted_index.proposed	170
abstract_inverted_index.research	3
abstract_inverted_index.speed-up	161
abstract_inverted_index.systems.	44
abstract_inverted_index.trackers	47
abstract_inverted_index.tracking	2, 20, 43
abstract_inverted_index.training	149, 177
abstract_inverted_index.Moreover,	119
abstract_inverted_index.algorithm	86
abstract_inverted_index.challenge	59
abstract_inverted_index.cluttered	29
abstract_inverted_index.connected	180
abstract_inverted_index.continues	4
abstract_inverted_index.developed	142
abstract_inverted_index.efficient	116
abstract_inverted_index.typically	39
abstract_inverted_index.occlusion,	23
abstract_inverted_index.processing	97, 131
abstract_inverted_index.real-world	15
abstract_inverted_index.transposed	154
abstract_inverted_index.variations	16
abstract_inverted_index.Convolution	50
abstract_inverted_index.accelerator	145, 172
abstract_inverted_index.appearance,	25
abstract_inverted_index.background.	30
abstract_inverted_index.limitations	77
abstract_inverted_index.performance	167
abstract_inverted_index.circuitside.	83
abstract_inverted_index.computations	112
abstract_inverted_index.degradation.	168
abstract_inverted_index.illumination	26
abstract_inverted_index.performance,	57
abstract_inverted_index.requirement.	70
abstract_inverted_index.applications,	35
abstract_inverted_index.approximation	108
abstract_inverted_index.interpolation	90
abstract_inverted_index.requirements.	103
abstract_inverted_index.significantly	94
abstract_inverted_index.<div>The	0
abstract_inverted_index.algorithm-side	80
abstract_inverted_index.implementation.	118
abstract_inverted_index.implementations	37, 62
abstract_inverted_index.online-training	123
abstract_inverted_index.state-of-the-art	56
abstract_inverted_index.frames-per-second	175
abstract_inverted_index.hardware-expensive	111
abstract_inverted_index.layers.</div>	181
cited_by_percentile_year
corresponding_author_ids	https://openalex.org/A5069552691
countries_distinct_count	1
institutions_distinct_count	3
corresponding_institution_ids	https://openalex.org/I145487455
citation_normalized_percentile.value	0.20378903
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False