The vast amount of video evidence available to 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.
A variety of sources contribute to the influx of video evidence, from security cameras and traffic footage to body cameras, dashcams, and mobile devices. Since 97% of Americans own a mobile device, such footage is now available in both the public and private sectors. Additionally, the widespread adoption of body-worn cameras by local police departments and sheriff's offices is further increasing the prevalence of video evidence. Over 47% of general purpose law enforcement agencies and over 80% of large police departments use body-worn cameras.
Using AI in video evidence examination
Traditionally, analyzing video footage required a labor-intensive manual review process, but advances in AI technology have enabled automation and rapid analysis of video evidence.
For example, instead of spending hours manually reviewing a 10-minute video, you can now analyze it within minutes. Similarly, AI algorithms track people of interest across multiple video files and formats and identify potential matches based on specific characteristics of the individual.
A crucial benefit of AI in public safety lies in its ability to quickly analyze extensive data sets in real time. AI platforms excel at detecting patterns, spotting anomalies, and predicting potential threats with high accuracy through the use of machine learning algorithms.
This capability enables law enforcement agencies (LEAs) to effectively address security issues and proactively and efficiently optimize resource allocation among first responders and other public safety stakeholders. Meanwhile, humans are included in the loop of automated processes, allowing team members to do their work. Get better data in a shorter time frame.
By leveraging certain AI solutions, LEAs can streamline video evidence analysis by connecting images from different files to build a comprehensive narrative of individuals, events, and timelines. This significantly increases the efficiency and effectiveness of investigations within and outside the legal realm.
Nevertheless, the use of AI in investigations has raised concerns about privacy laws and the protection of personally identifiable information (PII), particularly how facial recognition technology can be used without violating these rights. The focus is on.
Fortunately, with the advent of cutting-edge AI technology, alternative approaches are now available for tracking people of interest across video files without relying 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 can violate an individual's privacy. This approach not only speeds up the analysis process but also reduces privacy risks associated with video surveillance.
Prioritize privacy without sacrificing speed
The importance of time cannot be overstated. In cases involving missing persons, the first 48 hours are critical because the evidence remains fresh and the chance of finding the missing person increases. By leveraging AI to accelerate the review of video evidence, LEAs can increase their chances of finding missing persons and identifying persons of interest.
Human-like object (HLO) detection technology becomes essential in situations where facial recognition is not practical or ethical. With HLO detection, an AI engine identifies individuals based on specific characteristics that they are trained to recognize, such as clothing, piercings, and footwear. AI streamlines the process of reviewing extensive video footage by pinpointing instances where these characteristics appear, making it more time efficient.
Use cases for HLO detection include victim identification, suspect identification and arrest, and witness identification.
Other ways AI can help law enforcement locate individuals in video footage
Beyond identifying individuals without using facial recognition, AI can also do other things that can help human analysts and investigators track people, establish critical timelines, and gather critical information. provide a method. This frees analysts and researchers from tedious tasks and allows them to spend more time doing their jobs. community.
Big data and predictive analytics
In the area of search capabilities, AI is revolutionizing big data and predictive analytics, delivering important advances such as:
- Predict someone's likely location and behavior patterns using extensive datasets of social media content and public records.
- Predictive modeling allows investigators to adjust search parameters and direct resources to areas poised to have the greatest impact.
- Leverage natural language processing (NLP) techniques to sift through social media posts and extract valuable insights that power your efforts to find people of interest.
geospatial analysis
Terrain mapping and analysis using Geographic Information Systems (GIS) plays a vital role in supporting search and rescue operations. AI integration automates these processes and improves the accuracy of geospatial data analysis. This automation allows researchers to quickly process large data sets and accurately identify patterns that may be missed using traditional methods.
vehicle tracking
Tracking an individual across video footage only works if the person is visible on camera, which can be an issue if they enter a vehicle. To address this, there are AI tracking solutions that can seamlessly transition from tracking people to tracking vehicles. In this way, police can identify individuals and maintain the integrity of the incident 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 companies. Through this type of partnership, the development of more powerful and efficient AI-driven tools will be possible, amplifying the effectiveness of search and rescue operations and extending it to other related applications. One such prospect is leveraging AI for early detection and intervention strategies to forestall disappearances through robust monitoring and analysis.
As technology continues to advance, we can expect to see the emergence of new AI-powered tools and methodologies, including advanced biometric capabilities and sophisticated predictive modeling techniques.
Having access to the right tools remains essential for public safety agencies to deal with the evolving investigative landscape, and the introduction of AI that makes LEAs more effective, accurate, and easier to use is a powerful This will be a great step forward.
Final thoughts: AI helps maintain balance between privacy and public safety
As AI is increasingly integrated into law enforcement, striking a balance between protecting privacy and ensuring public safety becomes paramount. While AI has the potential to enhance public safety measures, it also has the potential for invasion of privacy and abuse of power. With the right safeguards and practices, AI can be used to serve and support the greater good.
It will be important for organizations to establish ethical and legal frameworks to govern the use of AI and protect privacy rights. This requires the development of legal initiatives and guidelines aimed at promoting transparency, accountability, and oversight of AI-driven systems.
It is also important to implement best practices that reduce the inherent risks associated with AI technology, such as data anonymization and strict security protocols. Ultimately, prioritizing privacy will continue to be a fundamental pillar of public safety efforts and foster public trust in law enforcement.
