Algorithm for the Analysis of business district using Pedestrian-Detection

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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COMPARATIVE ANALYSIS OF HOG WITH LINEAR SVM AND SSD ALGORITHMS FOR REAL-TIME PEDESTRIAN DETECTION

10.37099/mtu.dc.etdr/806 ◽

2019 ◽

Author(s):

Kalpesh Kuber

Keyword(s):

Comparative Analysis ◽

Real Time ◽

Pedestrian Detection ◽

Linear Svm

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Perception

10.1093/oso/9780199674923.003.0025 ◽

2018 ◽

Author(s):

Joel Z. Leibo ◽

Tomaso Poggio

Keyword(s):

Computational Neuroscience ◽

Feature Detection ◽

Pedestrian Detection ◽

Detection Systems ◽

Catching Up ◽

Feature Detectors ◽

Recognition Systems ◽

Engineered Systems ◽

Perceptual Systems ◽

The Brain

This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.

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