Humanoid Robot Detection Using Deep Learning: A Speed-Accuracy Tradeoff

2018 ◽  
pp. 338-349 ◽  
Author(s):  
Mohammad Javadi ◽  
Sina Mokhtarzadeh Azar ◽  
Sajjad Azami ◽  
Saeed Shiry Ghidary ◽  
Soroush Sadeghnejad ◽  
...  
Keyword(s):  
Deep Learning ◽  
Humanoid Robot ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

scholarly journals Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4491
Author(s):  
Deisy Chaves ◽  
Eduardo Fidalgo ◽  
Enrique Alegre ◽  
Rocío Alaiz-Rodríguez ◽  
Francisco Jáñez-Martino ◽  
...  
Keyword(s):  
Deep Learning ◽  
Face Detection ◽  
Processing Time ◽  
Criminal Activity ◽  
Absolute Error ◽  
Learning Approaches ◽  
Linear Regression Models ◽  
Original Size ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

Neuroticism and Speed-Accuracy Tradeoff in Self-Paced Speeded Mental Addition and Comparison

2010 ◽  
Vol 31 (3) ◽  
pp. 130-137 ◽  
Author(s):  
Hagen C. Flehmig ◽  
Michael B. Steinborn ◽  
Karl Westhoff ◽  
Robert Langner
Keyword(s):  
Reaction Time ◽  
Response Speed ◽  
Time Variability ◽  
Reaction Time Variability ◽  
Comparison Task ◽  
Processing Efficiency ◽  
Mental Addition ◽  
Speed Accuracy ◽  
The Relationship ◽  
Accuracy Tradeoff

Previous research suggests a relationship between neuroticism (N) and the speed-accuracy tradeoff in speeded performance: High-N individuals were observed performing less efficiently than low-N individuals and compensatorily overemphasizing response speed at the expense of accuracy. This study examined N-related performance differences in the serial mental addition and comparison task (SMACT) in 99 individuals, comparing several performance measures (i.e., response speed, accuracy, and variability), retest reliability, and practice effects. N was negatively correlated with mean reaction time but positively correlated with error percentage, indicating that high-N individuals tended to be faster but less accurate in their performance than low-N individuals. The strengthening of the relationship after practice demonstrated the reliability of the findings. There was, however, no relationship between N and distractibility (assessed via measures of reaction time variability). Our main findings are in line with the processing efficiency theory, extending the relationship between N and working style to sustained self-paced speeded mental addition.

Linking Personality With Basic Cognition: Need for Closure vs. the Speed-Accuracy Tradeoff

2014 ◽  
Author(s):  
Babette Rae ◽  
Scott Brown ◽  
Maxim Bushmakin ◽  
Mark Rubin
Keyword(s):  
Need For Closure ◽  
Speed Accuracy ◽  
Basic Cognition ◽  
Accuracy Tradeoff

Speed/accuracy tradeoff in directed forgetting

1997 ◽  
Author(s):  
Jeffry S. Kellogg ◽  
Xiangen Hu ◽  
William Marks
Keyword(s):  
Directed Forgetting ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

scholarly journals On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

2020 ◽  
Vol 13 (1) ◽  
pp. 89
Author(s):  
Manuel Carranza-García ◽  
Jesús Torres-Mateo ◽  
Pedro Lara-Benítez ◽  
Jorge García-Gutiérrez
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Remote Sensing Data ◽  
Autonomous Driving ◽  
Two Stage ◽  
Detection Systems ◽  
One Stage ◽  
Time Requirements ◽  
Speed Accuracy

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.

Trading accuracy for speed over the course of a decision

2021 ◽  
Author(s):  
Gerard Derosiere ◽  
David Thura ◽  
Paul Cisek ◽  
Julie Duqué
Keyword(s):  
Late Stage ◽  
Human Subjects ◽  
Sensory Information ◽  
Decision Task ◽  
Unique Extension ◽  
Dynamic Changes ◽  
Performance Accuracy ◽  
Accuracy Criterion ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

Humans and other animals often need to balance the desire to gather sensory information (to make the best choice) with the urgency to act, facing a speed-accuracy tradeoff (SAT). Given the ubiquity of SAT across species, extensive research has been devoted to understanding the computational mechanisms allowing its regulation at different timescales, including from one context to another, and from one decision to another. However, animals must frequently change their SAT on even shorter timescales - i.e., over the course of an ongoing decision - and little is known about the mechanisms that allow such rapid adaptations. The present study aimed at addressing this issue. Human subjects performed a decision task with changing evidence. In this task, subjects received rewards for correct answers but incurred penalties for mistakes. An increase or a decrease in penalty occurring halfway through the trial promoted rapid SAT shifts, favoring speeded decisions either in the early or in the late stage of the trial. Importantly, these shifts were associated with stage-specific adjustments in the accuracy criterion exploited for committing to a choice. Those subjects who decreased the most their accuracy criterion at a given decision stage exhibited the highest gain in speed, but also the highest cost in terms of performance accuracy at that time. Altogether, the current findings offer a unique extension of previous work, by suggesting that dynamic changes in accuracy criterion allow the regulation of the SAT within the timescale of a single decision.

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