Riot Investigations: How Video Evidence Technology is Changing the Game

Riots and civil disturbances that cause damage to property or result in injury happen for many reasons and create countless challenges for law enforcement seeking to keep their communities safe.

As video evidence becomes more and more prevalent, investigators are leveraging video technology to conduct in-depth riot investigations and hold criminal combatants accountable.

How is video evidence changing the game?

In this article, we first discuss a few factors contributing to the growth of video evidence. Then we look at a 10-minute video clip in which Forensic Video Analyst, Grant Fredericks, shares best practices when it comes to interrogating video evidence from riots.

Factors Affecting Video Evidence in Riots

Video evidence is now widespread. When a riot occurs, there are several important things to consider when reviewing video evidence.

More Video Evidence Than Ever Before 

In 1994, riots erupted in Vancouver after its hockey team lost the Stanley Cup. Investigators received 100 hours of VHS video footage and worked to identify agitators. In 2011, another riot occurred in Vancouver (also because the hometown hockey team lost in the Stanley Cup final).

This time, the city recovered over 5,000 hours of digital video footage. Grant Fredericks was called upon to examine the video evidence and he put together a team of 52 certified video analysts to carefully review and collate every image. Since that second riot in 2011, the availability of video evidence has continued to proliferate.

Social Media 

In the past, many video investigations relied strictly on security camera footage, but now witnesses and suspects post photos and videos of themselves or their friends engaging in criminal behavior.

The growth of social media platforms has created a treasure trove of additional video evidence. As we’ve experienced with the violence at the US Capitol on January 6, a vast underground pool of talented hackers and citizen-sleuths have created publicly available libraries of the most-wanted riot targets

These open-source depositories of visual evidence have provided prosecutors with critical evidence in their riot suspect indictments.

Mainstream Media 

Many news agencies have now developed video investigation units of their own. They work to uncover video evidence and identify suspects, much the same way that police investigators do. Learn more about how media outlets are leveraging video evidence during riots or other crime related stories in our webinar, How the Media is Collecting and Weaponizing Video Evidence, free when you subscribe to our newsletter.

Better Investigative Software

Until recently, investigators examining video evidence at their desks would limit their notes to ‘Observations’ or a ‘Shot List’ written on legal notepads.  It was a tedious and time-consuming process. They were almost always frustrated by technical difficulties and complicated proprietary players.

With this method, hand-written timestamps or video runtime notes provided the only links to critical visual evidence. In an era when video was scarce in criminal investigations, shot-listing video observations on paper was a reasonable process.

The expansion of video evidence, however, means that many cases involve dozens and sometimes hundreds of hours of visual evidence. In other words, not only is the hand-written method archaic, but it is also an obstruction to an effective investigative workflow.

Fortunately, times are changing and specialized investigation software like iNPUT-ACE enables officers to create instant digital tags and notes that are searchable and can be shared with an entire investigation team.

Riot Investigation Workflow: Video

Grant Fredericks has led technical investigations of civil disturbance and riot events in the US, Canada and in the UK. In the video below, he highlights the modern digital workflow that investigators use to identify suspects and expedite criminal investigations.

This 10-minute clip is an excerpt from the 2020 Video Evidence Symposium. If you enjoy the video, then make sure to check out our full library of on-demand training content. We have eighteen different 90-minute training videos available from experts like Mr. Fredericks.

Watch the video or check out the transcript below.



The majority of the work that I [Grant Fredericks] have been doing is more of the dynamic and detailed investigations. And the reason for that is that when I get involved in a case, we usually have a large amount of video that requires the kind of analysis that you’ve demonstrated to really flesh out the opportunities for the investigators.

This whole project with iNPUT-ACE really was born from a significant investigation that I was involved in back in about 2011.

Back in 2011, the Vancouver Police Department, where I was prior, that’s where I was a police officer and I was in charge of the forensic video unit. They had a riot in 2011 after the Stanley Cup event. And the police chief there asked for the public to provide any video evidence that they may have collected primarily from cell phones or any of the public video cameras that were on the streets.

As a result of that, the department received about 5,000 hours of proprietary digital video. I suspect that a lot of the events that are occurring this week (June 2020), where you have rioting and looting and significant property damage, assaults, shootings, I suspect that those events will also have many hundreds of hours of video applied.

What we did is we brought together a team of qualified, trained video analysts to assist the Vancouver Police Department. This was before any of this technology was really available. We had about 50 investigators come into a facility that we set up and we put together a database. The database was not as developed or as advanced as what you’ve got with iNPUT-ACE, but it was a good start.

And what we did is we ended up going through these 5,000 hours of video, the total team working together over about two weeks in a lab.

We worked three shifts, 24/7. We ended up tagging 15,000 criminal acts committed by about 800 identifiable people. And how did we identify those people? Well, what we did is we started off by identifying clothing, obviously, male, female, race, even size, shape, but primarily clothing, what they were wearing. We then fed that into a separate database, searched through that, and were able to then coordinate the tagging.

So we could say, “Hey, this same person that is looting a store is also flipping this police car.” So as a result of that, we were able to get a significant result and about 800 people were charged and convicted for that riot. Since then, we’ve now brought all of that technology into iNPUT-ACE so that now a major investigation could use iNPUT-ACE and is using iNPUT-ACE to begin to track these cases.

So this week we looked at some of the riots that are occurring throughout the country. And we’ll show you right now, if we can just go to it, some of the video that we’re seeing. And before we get to that, that whole process, by the way, won the International Association of Chiefs of Police top award for technical advances in a criminal investigation. It was the August Vollmer Award, which we picked up at the ICP Conference for that project. So we’re very proud of that.

Anyway, that technology has now been ported over to iNPUT-ACE. And let’s take a look at a current riot video that we’ve been looking at for one particular investigation. And it’s all been loaded up here. So all the data that we’ve been provided has been brought into this case. We’ll take a look at one of the rioting events where this is looting. And we can see individuals coming out. Let’s stop it right here. Let’s tag this person. Let’s put this into this project.

I’m going to tag this guy and I’m going to look at different features. So let’s take a look at this looter. So we’ll just type in looting. We can add narrative if we wanted to, but I’m just going to add a description. So this is a male. So we’ll go to gender and we’ll just say he’s a male. We can tag in things like the shirt. So let’s go down to shirt and we’ll just say black and white checkered shirt. So we could add a lot of other data in here, whatever you want. You can customize your data as well. You could type something in here, “any tattoos that might be visible” and we could give a full description.

So all of that then is searchable. And we’ll give you a quick example of how effective this process is. This is an individual a couple of days ago, who was lighting a Molotov cocktail. So we’re going to just tag that individual. We’ve already actually got pre-objects tagged with him. So I’ll just open this up again and show you. So we’ll open up the markers, and we can see that he’s been identified as a white male. He’s got green gloves on. In one of the videos we can see he’s got the green gloves, he’s got the Molotov cocktail in his hand, and he’s a male.

So has this individual ever shown up before? So another investigator might’ve been looking at a different video at some point, and has tagged this person because they see he’s involved in a criminal act. So what we’re going to do is we’re going to sort through all of the video. So we could have hundreds, maybe even thousands of observations as we did with the Vancouver riots. And we’re simply going to say, let’s sift for gender. Let’s go to male because we’re going to look for this individual. And then we’re also sift for, let’s say gloves. I want green gloves. So anybody we see who is a male wearing green gloves, let’s say we’re going to use contains, and we’ll just say green.

We’ll filter through that. And sure enough, we come up with two individuals. One of them is the male with the Molotov cocktail. And then in this one here we see an individual walking with no mask on. Notice the other one had a mask on. So let’s go back to that face shot and zoom into it. And if we zoom in, we can get some now identifiable features before he lifted his mask. So these brain surgeons out there, they walk around with no face covering. Once they’re going to do their criminal act, they’re going to put their facial covering on. So we can easily lead the investigators to some identifiable features that could then go into a BOLO Report.

So that gives you a really quick example of how the sift and sort function works in these large scale investigations, especially these riot type investigations where you might have multiple teams or multiple investigators tagging and infusing metadata into their video evidence. It is a very effective process.

The kinds of opportunities that are available to police agencies is that if you just have a small section, one or two investigators, the solutions are you can get a dongle license that allows you to do all of this kind of stuff with just one or two investigators.

If you have multiple units or a whole agency where your investigators have to look at video evidence, you can do the same kind of work, but in an enterprise-wide solution. So every investigator in your agency could have access to the ability to do this kind of work, instantly acquire video evidence, save these projects, collaborate on large scale investigations, and move these cases forward.

If anybody has any questions on how to do this large scale investigation work, especially for these riot type civil disturbance cases, let us know. And we’d be happy to put together a webinar exclusively for teams that are trying to attack these kinds of large-scale cases.

I know that no city wants to have to put up with this kind of attack and criminal behavior on the business community. This is not part of the legitimate demonstrations that we’re seeing today. These are agitators who are really trying to cause a lot of damage and a lot of harm. And so I know those are the targets of most police agencies, and we’re really happy to help show them how they can do that, kind of work themselves.

QUESTION: How easy is it to learn how to tag video evidence for the basic investigator?

I’ll grab that real quick. I mean, if you can make an observations and type the observation into iNPUT-ACE, I mean, that’s it.

Most investigators today, if they don’t have this kind of a tool they’re reviewing video evidence that they can view if they have the right players and they’re able to work with their computer, and they’re making the observations, and they’re keeping a video shot list, and it’s usually a handwritten shot list on legal paper. This is a much more dynamic process.

If you are used to making observations on your shot list, let’s just automate it and make it electronic and searchable.

QUESTION: In regards to riot investigations, generally speaking, how are the tags defined or how were investigators trained or told to tag items to best allow for filtering sorting and sifting later on?

That’s a great question because in Vancouver, we actually had a code because this was a riot involving a hockey event. So the rioters, many of them were wearing different generations of Vancouver Canuck hockey shirts. And so we actually had to rather than say, “Vancouver Canuck hockey shirt,” we actually had a code for the different years of hockey shirts.

So each event might be a little bit different. For instance, we worked on an Antifa case where the Antifa people were dressed in black. So we were looking for uniqueness.

And everyone, even though they were addressed almost fully in black, some of them were wearing a pink backpack. Some of them had white laces. Some of them had boots rather than shoes. And so we were able to tag affiliation Antifa, and then also what was unique about it. And so not just black everything, but the unique features and characteristics.

So you can define whatever your particular needs are, depending on the kind of investigation you have. So you can use any kind of tagging methodology.

Want to learn more?

If you want to learn more about how to effectively investigate video evidence from riots, accidents, crimes, or anywhere else, then feel free to contact us.

This 10-minute clip originally appeared as part of our 2020 Video Evidence Symposium. Dig deeper by checking out our full library of on-demand training symposium content.