Master Thesis Intern
Axis Communications
Master Thesis Intern
Axis Communications
Network-based security cameras and access control
For this thesis project, there are 5 different ideas. Read through them all and see which ones interest you. Final idea is decided together with the supervisor/manager.
Category
Electrical or Computer Engineering, Engineering Physics, Mathematics and similar
Scope
Two Master Students, 30hp each
Relevant courses
Efficient C (or similar), Automatic Control, possibly Algorithms and Data Structures (mostly for the algorithm aspect)
Background
Standard real time system bitrate control algorithms are reactive, using the size of previously encoded frames to control the video encoding. While latency is always bad, this can be particularly problematic as the algorithm needs to be reactive enough for scenarios where there are large, sudden changes, e.g. a train passing by, but also not be too reactive to create self-oscillations.
There are however other indicative data available in our camera video pipeline that can estimate the amount of current motion in the scene and that is available several frames ahead of it's encoding which could be used to improve the bitrate control algorithm.
Goal
Evaluate the benefits of using future motion indicative data in video bitrate control algorithms.
As there are several options for the data types and how far into the future we can see, a good side goal would be to evaluate them.
Relevant courses
C/Python (good to know), Mathematical Statistics
Background
Standard objective video quality metrics (e.g. PSNR, SSIM, VMAF) all have issues in determining the surveillance quality of encoded video.
A novel approach would to define new metrics based on the ability of different detectors to detect, classify, recognize and identify different object types. This would mean using e.g. the same analytics application on the raw video and various encoded versions of the video (where information and thus detection capabilities are lost). At far distances this will impact the ability to detect objects ("there is something"), at long distances the ability to classify objects ("it's a human"), at medium distances the ability to recognize objects ("it's the same human") and at short distances the ability to identify the object ("it's Mr. Smith"). In practice, a suite of different analytics applications would likely be the best.
The metric needs to be tuned to handle different light conditions and object distances and false detections might need to be handled somehow.
Goal
Create a capability based objective video quality metric and investigate if this is suitable for measuring surveillance video quality.
Relevant courses
Mathematical Statistics , Efficient C, Algorithms and Data Structures
Background
The local weather conditions can have a large impact on the bitrate generated by the camera and thus our long term bitrate control algorithms could greatly benefit from knowing the current and future local weather conditions when controlling the video encoding. A typical use case would be a 30 day storage on an on camera SD-card, where we'd like to be able to prepare in advance for a coming storm, but also not end up in a scenario where a couple of stormy hours has filled up most of the storage for the full month, severely limiting the video quality for the rest of the time.
The weather estimation could be done either by an on camera neural network or pushed to the camera by a connected web service. This doesn't need to be part of the thesis.
Goal
Investigate if using weather data for the rate controller would be useful for long time storage control.
Relevant courses
Mathematical Statistics, C/Python
Background
Standard objective video quality metrics (e.g. PSNR, SSIM, VMAF) all have issues in determining the surveillance quality of encoded video.
A novel approach would to define a new metric based on the preservation of temporal changes in the video. As standard objective video quality metrics only compare the current frame with the raw current frame, it will not give enough weight to temporal compression artifacts. The temporal changes can be measured between two neighboring frames or for a weighted set of frames and themselves be evaluated using standard objective video quality metrics.
Goal
Create a temporal difference based objective video quality metric and investigate if this is suitable for measuring surveillance video quality.
Relevant courses
C/Python, Mathematical Statistcs, Communication Systems and Networks or Internet Protocols
Background
There is a need to be able to measure how badly a video is damaged during bitrate adaptation and transportation through unreliable networks. The damage to the video can be either seen as loss in video quality due to adaptation, packet loss and delayed or laggy video.
Goal
Inspiration from Structural Similarity Index Measure (SSIM)? Many different aspects to boil down to one value.
How to weight video quality loss, packet loss and network latency? TCP latency & retransmission? Do we have the video both before and after the transmission?
OK, I am interested! What do I do now?
You are valuable to us – how nice that you are interested in one of our proposals! There are a few things for you to keep in mind when applying.
Who to contact for any questions regarding the position!
Magnus Söderdahl at [email protected]
Certain roles at Axis require background checks, which means applicable verifications will be done in these recruitments. Notice will be provided before we take any action.
We enable a smarter and safer world by creating innovative solutions for improving security and business performance. As a network technology company and industry leader, we offer solutions in video surveillance, access control, intercom, and audio systems, enhanced by intelligent analytics applications.
With around 6,000 committed employees in over 50 countries, we collaborate with partners worldwide. Together, we thrive in our friendly, open, and collaborative culture and inspire each other to think beyond the expected. United by our commitment to inclusion, diversity, and sustainability, we consistently seek to develop our skills and way of working.
Let´s create a smarter and safer world
For more information about Axis, please visit our website www.axis.com.
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Network-based security cameras and access control
Axis Communications provides network-based security solutions for businesses, including IP cameras, network audio systems, and access control. It uses ThinServer Technology to optimize performance and scalability by integrating devices and software over IP networks. It differentiates itself with a broad, integrated portfolio of interoperable, enterprise-grade security solutions backed by the Canon Group. Its goal is to improve security and operational efficiency through reliable, scalable, and integrated network-based surveillance and access systems.
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