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In the past few years, artificial intelligence has emerged as one of the major powers to disrupt the way we are. Smart algorithms unburden businesses from time- and workforce-consuming routines, enabling them to make a huge leap towards increased agility and efficiency. Having a wealth of application areas, AI is well on its way to revolutionize object recognition and tracking, traffic control and video surveillance — the sky’s the limit.
Tech giants continue to invest in AI technologies that reshape how video is processed and interpreted. While companies like Alibaba are now focusing on generative AI models for multimodal video understanding, such as Qwen-VL and WAN 2.1, others like Huawei are advancing edge-based surveillance platforms with on-device event detection and real-time scene analysis.
At the same time, a wave of smaller companies is embedding AI directly into smart cameras and edge devices, enabling instant video analysis without relying on the cloud. This trend is accelerating — the global market for AI surveillance cameras is expected to grow from $5.63 billion in 2024 to $6.81 billion in 2025, with a CAGR of over 20%1. This shift supports real-time decision-making in environments where speed and autonomy are critical, from street-level incident detection to warehouse logistics and beyond.
What makes this shift so significant is not just the scale, but the intelligence behind it. AI doesn’t just detect, it adapts. It can distinguish between real threats and false alarms, follow an object through occlusions, or flag subtle changes in behavior before humans notice them.
Today’s smart video tracking software doesn’t just see what’s happening; it understands it. In industries where every second counts, this capability opens up new possibilities for automation, safety, and speed.
With dangers like gun violence getting increasingly common worldwide, the challenge of building reliable public safety solutions is well up in the air. Quite frequent are the cases when police officers shoot suspects, mistaking harmless objects they hold for guns. To stay alert and efficiently spot crimes and accidents avoiding failures in critical situations, police stations and emergency companies can now harness AI video systems.
Oxagile’s maturity in the video domain, complete with computer vision and machine learning engineering pool, became a good match for several projects around smart video surveillance. To help the clients increase the efficiency of their law enforcement solutions and keep hours of tiresome live monitoring hassle away, our team undertook the development of computer vision and facial recognition functionality that can be easily implemented into the client’s video environment.
Today, more and more agencies rely on AI-powered body cams, drones, and vehicle-mounted cameras to gain real-time situational awareness. By combining geolocation data, object detection, and smart video tracking software, these systems support faster and more accurate field decisions even in high-stress scenarios.
Building reliable software that pairs smart algorithms to static cameras stops being that much of an issue, as engineers worldwide have long started developing solutions to handle it. Yet, today the requirements to video processing systems grow more complex. With loads of videos made with smartphones or drone cameras, the new challenge is to create a tracking software applicable to on-the-go recording.
For one of our recent projects, we are building custom computer vision functionality that wrestles with the problem of detecting and tracking live video objects with a moving camera. This is a no mean feat for quite a number of reasons.
Camera’s motion implies vibration and changing speed that badly affect image quality with blurs, shadows, obstructions, and background clutters. Even a fixed outdoor camera cannot remain completely static and safe due to weather conditions or intentional damage.
Add to this the increasing use of body-worn video in law enforcement and first-response scenarios, where cameras must function under low lighting, fast movement, and partial occlusion, and the tracking challenge grows significantly.
Moreover, nearly each case requires a custom solution tailored to specific applications and business needs. Take, for instance, patrol officers that are to get equipped with body cameras, GPS, and drones under continuous threat of street shooting and riot. In this regard, last year a US police body camera manufacturer announced acquisition of an AI startup that develops object recognition software designed to train officers to better detect targets.
Similar demands are emerging in the private sector. Delivery services are using AI video motion tracking to monitor last-mile logistics, while manufacturing plants apply tracking to ensure worker safety on production lines. Smart video systems are no longer exclusive to security — they’re becoming foundational to how modern enterprises operate.
This growing demand has prompted major investment in real-time tracking algorithms tailored for edge devices, mobile contexts, and high-density environments. The result: flexible systems that adapt to use cases, not the other way around.
On a high level, the most common approach to AI video motion tracking is to detect the object of interest in the first frame and continue tracking it throughout the video using a chosen algorithm. While this works well with a fixed camera, it quickly becomes unreliable once the camera starts moving, due to image blur, shifting perspective, or background instability.
To find the best fit for different environments, we conducted extensive testing of tracking models in both static and dynamic scenarios. Below are some of the most notable approaches and what we learned from them.
Adaptive to the specifics of tracking a maneuvering object, the Kernelized Correlation Filter is a reliable approach to handling noise, obstructions, and changes in the target’s appearance. In a complex recording environment, it ensures better precision and is capable of keeping focus on the target for an extended time.
KCF performs well in high-frame-rate scenarios and with predictable motion. However, it may struggle in cases of fast occlusion or when the target moves out of frame entirely.
A go-to algorithm for tracking objects that move in a predictable linear manner across an area with few large obstructions. Dlib can handle simple motion tracking via static cameras, such as following athletes on a running track or pedestrians crossing a street.
However, it lacks robustness in mobile or highly dynamic environments. When combined with a shaking or fast-moving camera (e.g., drones or smartphones), Dlib tends to lose the target quickly.
Re3 performs well under inconsistent lighting conditions, moving cameras, and frequent obstructions. Thanks to its recurrent structure, it’s able to “remember” the target’s features and restore tracking after short-term loss of visual contact.
Among tested options, Re3 was the only model to reliably recover focus when the object disappeared and then reentered the frame. However, its weakness lies in ambiguity — if multiple visually similar objects are present (e.g., a person blending into a crowd), it may lose tracking fidelity.
One of the latest additions to the tracking toolkit, ByteTrack is known for maintaining high accuracy even in dense scenes with multiple moving targets. It separates detection from tracking, allowing more stable identity preservation.
ByteTrack shines in real-time multi-object tracking scenarios such as crowd monitoring, retail analytics, and sports events. However, it requires strong object detection models as a foundation, and its performance can degrade with inconsistent input quality.
STARK leverages transformer-based attention mechanisms to model object relationships across space and time. This allows it to anticipate object motion patterns, handle scale changes, and maintain tracking across abrupt shifts.
While computationally heavier than classical methods, STARK provides exceptional precision in high-complexity scenes and is ideal for use in AI video motion tracking where long-term consistency is required.
There is no single solution that performs best in all environments. Instead, selecting the right smart video tracking software means balancing algorithm capabilities against real-world constraints, including camera motion, environment complexity, and performance requirements.
In some cases, hybrid solutions work best, combining fast classical algorithms like KCF with deep learning models that refine predictions post-frame. In others — edge-deployed transformer models deliver accuracy and responsiveness even in disconnected or low-power environments.
Our growing expertise in developing computer vision and machine learning solutions for global businesses makes us sure that pairing video solutions to reliable tracking algorithms can translate into tangible gains for your businesses right away.
With AI video motion tracking, companies can move beyond passive video capture, gaining real-time visibility, faster response, and data-driven decision-making at scale. Whether in public safety, logistics, healthcare, or retail, smart tracking apps are transforming how teams monitor activity, detect anomalies, and act on critical events.
The result? Fewer manual hours, fewer missed incidents, and smarter, faster operations — without the need for constant human supervision.
If you need a video system that can help you save man-hours, cut operating expenses, and stay ahead of the competition, just contact us. Let’s talk, and we’ll design a value-based roadmap tailored to your goals.
1. The Business Research Company. Artificial Intelligence (AI) in Video Surveillance Global Market Report 2024
