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00:00 Hello everyone. My name is. a professor here in the department of

00:05 Science and I directly switch at the imaging laboratory. So my research interests

00:12 primarily in computer vision and video The heavily focus on understanding of human

00:19 , understanding of group activities as well seen understanding in video imagery along with

00:28 of anomalous and diverse events. We quite interested in being able to apply

00:35 algorithms specifically in this space to real scenarios. So as a result,

00:41 are also interested in looking at the systemic impact of video imagery and and

00:48 impact on specifically the quality of video . So we focus quite a bit

00:54 design of algorithms that are robust to artifacts that might be present in video

01:00 . Finally, we are also interested software development specifically in realization of software

01:08 where video analysis can be tested at , which means that we want to

01:14 able to deploy algorithms that can process video streams that are being transferred at

01:22 . We are specifically looking at challenges exist when we are deploying cameras in

01:29 world infrastructures that are designed family for safety type applications where they need to

01:38 large geographical spaces. Oftentimes the infrastructure supports this kind of video acquisition is

01:46 by a range of network infrastructures by as well as wireless, causing a

01:54 of artifacts to be embedded in the imagery that gets collected predominantly coming from

01:59 network artifacts or packet loss due to and so on so forth. We

02:05 look at how we can generally see for broader applicability because oftentimes in unconstrained

02:15 is very difficult to define exactly the or which specific analysis needs to be

02:23 . Um and and as a result have to be able to design custom

02:29 on the fly that may be able be deployed or to be adopted for

02:36 types of scenarios that one may And the third is an open challenge

02:42 the ability to provide the analyzed information a human consumable form, especially when

02:52 you are talking about hours and hours video imagery that needs to be

02:56 The information coming from them can be overwhelming for a human to be able

03:02 understand and take actionable steps of what do next. So to being able

03:07 provide the appropriate kind of information so humans can make the appropriate decision on

03:14 information that becomes available because of video . And that's a critical aspect that

03:19 look at as well. So I wanted to quickly highlight some of the

03:23 research efforts. First on the algorithmic , as I said, we quite

03:28 look at how we can understand video uh from acquired with the imagery in

03:33 time. We also look at algorithms we can potentially try and recover some

03:40 the lost information because of network So we can actually improve the quality

03:47 image that we are able to apply to at the same time. We

03:54 focus on design of video quality of algorithms for object protection, object tracking

04:00 well as object re identification. And , um as I said before,

04:06 design algorithms for understanding human activity as as abnormal events. So these are

04:13 areas that we are actively pursuing in research group and and of course these

04:18 some uh you know, fairly challenging . So we continue to explore different

04:25 on how we can improve algorithms in to analyze this information. Um

04:32 uh you know, I want to mention that we have been designing scalable

04:37 frameworks. We specifically leverage uh you , cloud architecture as well as you

04:44 , um kubernetes clusters and dockerized deployments be able to deploy algorithms so that

04:52 only we can test our algorithms that in the research group but we can

04:57 them available to other users, certainly , but also other end users that

05:03 often partner with to be able to our algorithms on their own video

05:08 Look forward to answering questions that you have uh in the coming days.

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