Today, with video cameras being ubiquitous, recording video is no longer sufficient. Businesses want smart video: systems that can see, understand, and act. This is when AI video analytics steps in. Here’s an overview of how three core processes—object detection, tracking, and recognition—combine and work as one. By the end, you will know what these mean, how they are different, what the challenges are, and how Tentosoft builds systems to overcome challenges.
What Is Object Detection, Tracking, and Recognition?
Before delving further, it’s useful to define these three terms in detail:
Object Detection is finding where objects are in video frames. This means to draw a box (or other shape) around things like: people, cars, animals, and so on. Object detection tells you what and where.
Tracking refers to exemplifying these detected objects, over time, across multiple frames. It keeps the user’s consciousness of identity in relation to objects while noting where they moved to.
Recognition refers to the identification or classification of what type of object it is (i.e., car vs. truck), or the identification of attributes (i.e., color, model, or sometimes even human face recognition), each once you have detected and maybe tracked.
In a sense, these are significant stages that build on each other: first detect, then track, then recognize (or classify). With each stage, you are layering intelligence.
How Does Object Detection Work?
We will outline below what is happening under the hood, in simpler steps:
Video Frame Input:
A camera (or video source) is providing frames – images over time.
Preprocessing:
The images are cleaned up, adjusting for light, tearing down noise, and perhaps adjusting resolution as needed. Everything is done to help the system see better.
Feature Extraction:
The AI, or computer vision model, is looking for patterns of features in each frame, edges, textures, shapes, etc. These “features” are hints as to where an object might be.
Candidate Regions / Proposals:
The system, proposes certain parts of the frame as “high probability” object locations (for example, divided into grids, sliding windows, or region proposals).
Classification & Localization:
For each candidate region, the system determines: Is there an object? What class of object is it? (that is – person, car, bag, etc.) And then borders or encircles it in a bounding box or some other shape.
Refinement / Non-Max Suppression:
Often the system identifies multiple proposals on the same object, so it refines itself by suppressing a duplicate or overlapping proposal, and keeps the best one (most accurate).
Modern object detection is accomplished with deep learning methods, primarily convolutional neural networks (CNN’s) – specifically because they learn features themselves from large numbers of labeled images – in contrast to older methods of machine learning, CNN’s tend to be more accurate, yet also require much more data and compute.
What Is Object Tracking, and Why It Matters
Once an object is detected in frames, companies nearly always want to keep track of it, not only knowing the object for that frame but for all time. Let’s talk about how tracking works and what the accompanying challenges are.
Tracking Purpose:
Tracking allows you to see movement, establish trajectories (like a person moving through a store), count objects, identify anomalous behavior (like loitering or running), understand traffic flow, etc.
Tracking Types:
Single Object Tracking (SOT) – All focus on tracking one identified object.
Multiple Object Tracking (MOT) – All focus on tracking multiple objects consistently.
How Tracking Works:
After detection, tracking algorithms relate objects detected to consecutive frames. They often work on a combination of motion information (the manner it is moving), appearance (how it looks), and in some cases via prediction (estimating the next potential move).
Challenges:
Occlusion of objects: object becoming blocked by another object.
Appearance change: approach change as in light, angle, size.
Object left view and re-entered view.
Object is moving very fast or the frame rate is extremely slow creating motion blur.
Research and solutions continue to increase accuracy, speed, and robustness.
Object Recognition and Classification: Going Beyond Detection
Detection informs you of an object’s presence and its location. Recognition/classification describes what the object is (or other properties). Detection, matching, recognition/classification has several components:
Categories versus properties:
Categories are broad, for example: car, person, bike. Properties can be finer; color, model, brand or just behavior (a person running vs a person walking).
Recognition:
Once a detection model detects an object, a recognition model then considers the object’s appearance (texture, shape, color, context) and recognizes/classifies it. Recognition models can be separate from the detection model; they can also be joint models that contain detection and recognition.
Types of recognition (classification):
General object classification (e.g., “car” vs. “truck” vs. “motorcycle”)
Facial recognition (when allowed and ethically reasonable)
Recognition of person behavior/actions (e.g., “a person holding an object,” “a person waving”)
Trade-offs:
More sophisticated recognition requires more data and computing. Finer distinctions also become more difficult in poor conditions (e.g., low light, occlusion). Ethical concerns around recognition using peoples’ images and privacy also matter significantly.
Putting It All Together: From Video to Actionable Insights
Step
What Happens
Why It Matters
Frame capture
Video camera captures live or stored video
Must get good quality, stable input
Detection per frame
Objects are located and labelled in each frame
Provides basic awareness
Tracking across frames
The same object is followed over time
Helps count, monitor behavior, detect unusual motion
Recognition/classification
Objects are identified in category or attribute
Enables specific actions (e.g., “car,” “truck,” “red car,” “face match”)
Event detection / rule checking
System watches for rules (e.g. “person in restricted zone,” “crowd forming,” “vehicle speeding”)
Allows alerts, responses
User interface / reporting
Visualization, dashboards, alerts, logs
Helps business decision making and action
Tentosoft’s systems design each stage to work together, optimizing for speed, accuracy, and usability.
Challenges, Limitations, and How Tentosoft Addresses Them
Every system has its drawbacks. Below are main obstacles with AI video analytics, as well as Tentosoft’s plans for addressing them or how it is working in that respect.
Lighting, Weather, Image Quality: Insufficient lighting and inclement weather decreases detection and recognition accuracy.
Tentosoft’s approach: Apply image preprocessing, intelligent exposure management, adapting models to account for fast- or slow-moving degraded weather, use domain adaptation sampling/retraining with real data.
Occlusion, Crowd Density: Objects may occlude each other, so face or artifacts may only be partially visible.
Tentosoft’s solution: Use tracking algorithms that will continue confidence for occluded observations, fuse appearance and motion clues, and smoothing across time.
False Positives and False Negatives: Incorrectly identifying and/or missing objects (wrong class).
Tentosoft’s tactic: Maintain a balance between the thresholds for detection; use context (with respect to scene) for determining presence; and leveraging multi-stage validation processes. To reduce consistency errors, collect feedback and re-train models after annotating errors.
Latency and Resource Usage: Real-time demand for video analytics requires fast processing while gathering large quantities of data from the video stream.
Tentosoft’s solution: Edge processing, optimize architecture for hardware, and communicate open relationship.
Privacy and Ethical Usage: Recognizing people or sensitive attributes can be controversial.
Tentosoft’s policy: Conceptually adhere to principles of applicable state law, anonymize the class whenever possible; design the detection to include opt-in/consent from the detector; make the personal object detection available for transparency.
Conclusion
AI video analytics blends object detection, tracking and recognition to give businesses an excellent opportunity to move beyond passive video recording and to actionable insight. It empowers operational, real-time visibility, smarter alerts, behavior analysis, and data-driven decision making.
At Tentosoft, we believe a failure to consider both the opportunity and the limitations is the key. If you concentrate on reliable detection, robust tracking, accurate recognition, and ethics, then businesses can deploy video analytics systems that truly add value. If you should consider using AI video analytics, then carefully thinking through each of these stages will help you choose or build a system that will work well for your context. Then, you can guarantee your success out of this technology.