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The re-identification problem in computer vision

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Deep Learning in Computer Vision

National Research University Higher School of Economics

3.7 (142 classificações) | 22K alunos inscritos

Curso 5 de 7 no Aprendizagem de máquina avançada Programa de cursos integrados

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and often demonstrated in movies and TV-shows example of computer vision and AI. Do you have technical problems? Write to us: coursera@hse.ru

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Avaliações

3.7 (142 classificações)

5 stars
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4 stars
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3 stars
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2 stars
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1 star
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SJ

Jun 12, 2018

Excellent course! Quiz questions are conceptual and challenging and assignments are pretty rigorous and 100% practical application oriented.

RR

Apr 19, 2019

Don't just read what's written on the projector. Try explaining it. And explain with code.

Na lição

Convolutional features for visual recognition

Module two revolves around general principles underlying modern computer vision architectures based on deep convolutional neural networks. We’ll build and analyse convolutional architectures tailored for a number of conventional problems in vision: image categorisation, fine-grained recognition, content-based retrieval, and various aspect of face recognition. On the practical side, you’ll learn how to build your own key-points detector using a deep regression CNN.

Content-based image retrieval7:06

Computing semantic image embeddings using convolutional neural networks8:23

Employing indexing structures for efficient retrieval of semantic neighbors9:09

Face verification6:27

The re-identification problem in computer vision5:23

Ministrado por

Anton Konushin
Senior Lecturer
Alexey Artemov
Senior Lecturer

Transcrição

In this video, we're going to learn what it is

re-identification problem in computer vision and what

are common deep learning approaches to handle this problem.

Person re-identification is the problem of identifying people across images

that have been taken using different cameras or across time using a single camera.

Re-identification is an important capability for

surveillance systems as well as human computer interaction systems.

In the slide, you may already notice certain difficulties associated with this task.

Let us look at these more closely.

Person re-identification usually needs to match the

person images captured by surveillance cameras working in wide angle mode.

Therefore, the resolution of person images are very low,

for instance around 48 by 128 pixels.

And the lighting conditions are unstable too.

Furthermore, the direction of cameras and the pose of persons are arbitrary.

And therefore despite years of efforts,

person re-ID remains challenging due to dramatic variations in

visual appearance and ambient environment caused

by different viewpoints from different cameras,

significant challenges in human pose across time and space,

background clutter and occlusions,

and of course different individuals that share similar appearances.

A standard approach to re-identification generally

follows the pipeline sent by face verification algorithms.

A typical re-identification system takes as input two images,

each of which usually contains a person's full body.

And outputs, either a similarity score between the two images or

a classification of the pair of images as same if the two images depict the same person,

or different if the images are of different people.

Given two person images,

they are sent to Siamese convolutional neural network.

A neural network architecture work which consists of two copies of the same network.

For two images, x and y,

a Siamese network can predict a label to denote

whether the image pair comes from the same subject or not.

Many applications need to rank the images in the gallery

based on their similarities to a probe image.

Therefore a typical Siamese net outputs a similarity score instead.

The structure of the Siamese net is shown in the slide.

It is composed of two convolutional neural networks connected by a connection function.

Connection function is used to evaluate the relationship between two samples.

And cost function is used to convert the relationship into a cost.

How to choose the connection function and cost function is

closely related to the performance of the re-identification model.

There are many distance similarity or other functions that can be

used as candidates to connect to vectors such as euclidean distance,

cosine similarity, absolute difference,

vector concatenation and so on.

One detail concerning person re-ID problem is of the aspect ratio of width to

height of cropped images contained in persons is something on the order of one to three.

Thus it makes sense to crop overlapping parts of

person image in the way depicted in the slide.

The crops are then passed into a number, say three,

of sub-networks to perform effective feature extraction at the original scale.

Without the crops, one would need to resize the image to the square for instance,

and that would affect the scale of features,

which would otherwise be different.

Model architecture results in a part-based network.

First, the three parts may share the first convolutional layer since

the lowest level features are supposed to be the same for all parts of the image.

Second, each part may have their own set of convolutional layers,

which can help to learn per specific filters.

Third, the higher level features of all parts may be fused

at fully connected layer by some rule, for instance.

Then the similarity of fused features is evaluated by the connection function.

Driven by a common cost function,

the three parts can contribute to the training process jointly.

So person re-identification aims to establish person identity using

images captured from multiple cameras with non overlapping views.

This problem is really difficult and in fact,

unsolved fully due to large pose and lighting variation, clutter and occlusions.

A standard approach to re-ID is to learn a similarity score between two images,

and that's similar to face verification.

CNNs are a natural choice for first solving re-ID and furthermore,

you can approach this problem within the part-based framework.

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