The sheer volume of video evidence available for investigative teams has reached unprecedented levels. According to the Bureau of Justice Assistance, approximately 80% of crimes involve some form of video evidence, and this trend shows no signs of slowing down.
Various sources contribute to this influx of video evidence, ranging from security cameras and traffic footage to body cameras, dashcams, and handheld devices. With 97% of Americans owning a mobile device, the availability of such footage has become ubiquitous in both public and private sectors. Moreover, the widespread deployment of body-worn cameras by local police departments and sheriff’s offices further amplifies the prevalence of video evidence: over 47% of general-purpose law enforcement agencies and 80% of large police departments use body-worn cameras.
Using AI in video evidence review
Traditionally, analyzing video footage required labor-intensive manual review processes, but advancements in AI technology have enabled automation and expedited analysis of video evidence.
For instance, a 10-minute video can now be analyzed within minutes instead of hours spent on manual review. Similarly, AI algorithms can track persons of interest across multiple video files and formats, identifying potential matches based on specific features of individuals.
A pivotal benefit of AI in public safety lies in its capacity to swiftly analyze extensive data sets in real time. Through the use of machine learning algorithms, AI platforms excel in detecting patterns, spotting anomalies, and forecasting potential threats with heightened precision.
This capability empowers law enforcement agencies (LEAs) — among first responders and other public safety stakeholders — to effectively tackle security issues and optimize resource allocation proactively and efficiently, all while keeping humans in the loop of the automation process and empowering these team members to work with better data in a faster timeframe.
By leveraging certain AI solutions, LEAs can streamline video evidence analysis by connecting images across different files to construct a comprehensive narrative of individuals, events, and timelines. This significantly enhances the efficiency and effectiveness of investigations, both within and outside the legal realm.
Nonetheless, the use of AI in investigations has sparked concerns regarding privacy laws and the protection of personally identifiable information (PII), with a particular focus on how facial recognition technology can be employed without infringing upon these rights.
Fortunately, with the emergence of cutting-edge AI technologies, there’s now an alternative approach to tracking persons of interest across video files that doesn’t rely on facial recognition.
AI that protects PII
There are alternative AI models that prioritize the integrity of PII, allowing investigators to identify relevant information without relying on facial recognition or other biometric markers that could compromise personal privacy. This approach not only expedites the analysis process, but also mitigates privacy risks associated with video surveillance.
Prioritizing privacy without sacrificing speed
The importance of time cannot be overstated. In cases involving missing persons, the initial 48 hours are crucial, as evidence remains fresh, and the likelihood of locating the individual is higher. By leveraging AI to accelerate the review of video evidence, LEAs can increase the likelihood of finding missing persons and identifying persons of interest.
In situations when facial recognition isn’t practical or ethical, human-like object (HLO) detection technology becomes indispensable. With HLO detection, an AI engine identifies individuals based on specific features it has been trained to recognize, such as clothing, piercings, or footwear. By pinpointing instances where these features appear, the AI streamlines the process of reviewing extensive video footage, thus enhancing time efficiency.
Use cases for HLO detection include victim identification, suspect identification and apprehension, witness identification, and more.
Other ways AI helps law enforcement locate individuals in video footage
Aside from identifying individuals without the use of facial recognition, AI offers other methods that can help human analysts and investigators track people, establish important timelines, and gather important information—freeing them up from tedious tasks so they can dedicate more of their time to their communities.
Big data and predictive analytics
In the realm of search capabilities, AI is revolutionizing big data and predictive analysis, offering crucial advancements:
- Extensive datasets, comprising social media content and public records, are harnessed to anticipate someone’s potential locations and behavioral patterns.
- Predictive modeling empowers investigators to refine search parameters, directing resources to areas where they are poised to yield the greatest impact.
- Natural language processing (NLP) techniques are leveraged to sift through social media posts, extracting valuable insights that enhance efforts to locate persons of interest.
Geospatial analysis
Utilizing Geographic Information Systems (GIS), terrain mapping and analysis play pivotal roles in aiding search and rescue operations. With AI integration, these processes are automated, enhancing the precision of geospatial data analytics. This automation allows investigators to quickly process vast datasets, pinpointing patterns that could be overlooked when using conventional methods.
Vehicle tracking
Tracking individuals across video footage only works if they are visible to the camera, which can become an issue if they get inside a vehicle. To respond to this, there are AI tracking solutions that can seamlessly transition from tracking people to tracking vehicles. This way, police can still locate individuals and maintain the integrity of the case’s timeline.
Future trends and applications of AI in missing person investigations
The trajectory of AI in public safety is poised for collaboration between LEAs and technology firms. Through this type of partnership, the development of more powerful and efficient AI-driven tools is possible, amplifying the effectiveness of search and rescue endeavors and extending to other pertinent applications. One such prospect involves leveraging AI for early identification and intervention strategies to preempt disappearances through robust monitoring and analysis.
As technological advancements continue to unfold, we can anticipate the emergence of new AI-powered tools and methodologies that may encompass heightened biometric recognition capabilities and refined predictive modeling techniques.
For public safety agencies, accessibility to the right tools remains imperative in navigating evolving investigative landscapes — and adopting AI that can make LEAs more effective, accurate, and more readily available to serve is a strong step forward.
Final thoughts: AI helps maintain a balance between privacy and public safety
With the increasing integration of AI in law enforcement, striking a balance between safeguarding privacy and ensuring public safety emerges as a paramount concern. While AI holds the promise of bolstering public safety measures, it also carries the potential for privacy infringements and the abuse of authority. With the right safeguards and practices, AI can be used to serve and support the greater good.
It will be crucial for organizations to establish ethical and legal frameworks to govern the use of AI and safeguard privacy rights. This necessitates the development of legislative initiatives and guidelines aimed at fostering transparency, accountability, and oversight over AI-driven systems.
It will also be important to implement best practices, such as data anonymization and strict security protocols, which will help mitigate the inherent risks associated with AI technologies. Ultimately, prioritizing privacy will continue to stand as a fundamental pillar of public safety initiatives, fostering public trust in law enforcement.
