The field of AI video recognition has recently experienced a lot of growth. Video footage has become more readily available with cameras on all smartphones and surveillance systems set up in public places.
As such, videos are now being used everywhere, from entertainment to security monitoring systems to healthcare facilities, resulting in an upsurge in information flow. This makes video recognition essential for analyzing such massive data sets.
According to Gartner, by 2026, about 30% of businesses will view the isolated use of identity verification and authentication solutions as insufficient due to the spread of AI-generated deepfakes.
Here, we will discuss some basic information about AI video recognition, focusing on the most important computer vision and machine learning ideas behind it. Furthermore, we will unravel how this concept is used in various practical fields, examine what issues are encountered while developing these systems, and share how deep learning is employed to solve these problems.
So, by the end of this article, you will comprehend what video recognition is, how it functions, and why it has such an impact on our lives today.
What is AI Video Recognition?
Video recognition AI analyzes the content of a video stream to understand it, which encompasses detection, tracing, and identification of objects, scenes, and activities. This is one of the significant constituents of computer vision, which is all about interpreting visual data from the environment in an automated manner.
The main purpose behind the recognition of videos is to decipher relevant information from unwrought video data and transform it into a structured format that can be useful for further analysis or decision-making.
Video recognition software can come in handy when dealing with complicated systems by providing significant insights or automating activities that are considered boring and repetitive. For instance, it can be used by self-driving cars to detect or track vehicles and pedestrians and recognize traffic signs. At the same time, healthcare monitoring systems can identify falling events or other acts of motion.
Video Object Recognition with TensorFlow API
TensorFlow is one of the most widely used open-source AI libraries and is the most efficient software for recognizing videos. It can quickly identify objects within a video using GPU acceleration technology.
TensorFlow’s architecture allows for the detection and tracking of moving objects through motion analysis, which helps design responsive gameplay, improve security systems, and create visually appealing interface design elements in user experience design.
Additionally, it has several available useful libraries, such as Faster R-CNN and Mask R-CNN, and can act as an interface to other networks like YOLO.
Artificial Intelligence Technologies for Video Recognition
Many companies have become very skilled by using primarily open-source tools for visual information analysis. Consequently, today, we have a wide range of high-performance & platform-independent libraries and databases used in our day-to-day activities without coming down from cloud nine.
YOLO (You Only Look Once)
YOLO is an independent video detection system that operates in actual time at a very high frame-per-second. The latest release of YOLO employs an FCNN (fully convolutional neural network) to make multiple concurrent predictions for bounding boxes.
ImageAI
A library that does machine learning tasks using Python to analyze and recognize videos is ImageAI. ImageAI is often utilized to provide frameworks such as RetinaNet, YOLO V3, and TinyYOLO V3. Although it has the PyTorch backend now, ImageAI has yet to have a mature ecosystem or be flexible enough as it does not compete with others.
TorchVision
TorchVision add-on uses GPU (a graphics processing unit) acceleration. It is controlled by Facebook. The primary video datasets that this platform supports include CelebA, Cityscapes, ImageNet, and KITTI. Pre-trained models in TorchVision are available to solve AI video recognition problems, among other functionalities.
SSD Multibox (Single-Shot Detector)
The SSD Multibox represents a video recognition tool based on the Caffe technology. It can employ a sole neural network to craft a feature map so that objects in confined regions of a photograph can be distinguished in terms of their probabilities.
Major Challenges in AI Video Recognition
In the last few years, exceptional progress has been made, but certain difficulties remain in creating precise and robust video recognition systems. Some of the significant problems with video recognition include:
Scarcity of Labeled Data
A problem that arises in AI video recognition is when only a small amount of data has been assigned labels or tags showing what is in a certain place or event, like in the case with the video. Creating tags or labels that help us look for things and actions in the video is what we refer to as labeled data.
Nevertheless, labeling video data consumes a lot of time and resources; thus, getting a lot more becomes difficult. Consequently, researchers have developed techniques like semi-supervised learning and active learning that can reduce the quantity of labeled data during training.
Real-Time Performance
Most surveillance systems and self-driving vehicles already need video recognition on a real-time basis. To work at these speeds, the computers behind video recognition should also be capable of processing and analyzing video from 30 frames every second or even faster in some cases. Reaching such performance levels may prove difficult, especially when deep learning approaches are used since they are usually cumbersome in terms of computations.
One of the fundamental characteristics of video data is their high-dimensional nature, where each frame contains several million pixels. This situation is complex given the necessity of efficiently processing and analyzing them. For example, if one were to analyze a one-minute video clip at 30 frames per second, one would be required to process over 100 million pixels.
Other notable challenges in AI video recognition include varied appearances and high variability. Different lighting conditions or camera angles can make the same object appear different. At the same time, different objects might look the same or even appear identical, posing a challenge in identifying them.
Complexity of Object Interactions and Activities
Video recognition AI is concerned with recognizing individual objects within frames and their interactions and activities. For instance, to recognize a person walking, it should identify the person in one frame and detect their temporal movement direction.
Likewise, recognizing a group of people playing soccer entails identifying its players, at a minimum, the ball and their movements or actions. Making accurate, reliable video recognition software out of these complex interactions and activities is difficult.
How Does Video Recognition AI Work?
In deep learning, video recognition uses large datasets comprising several training examples for each possible input condition. Such training sets must include sufficient information about target object classes or activities expected during operationalizing neural network capability targeting object/activity identification tasks in videos.
This results from the need to train these neural networks across a spatial and temporal domain by discovering how object-category boundaries should be represented to identify such objects regardless of their varied poses within three-dimensional space like humans do when recognizing things around them.
Two common neural network types used for AI video recognition are 3D Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). 3D CNNs excel at spatial-temporal analysis, while RNNs handle sequential data effectively. These powerful tools enable machines to interpret and understand visual content with remarkable accuracy.
Audio-Visual Event (AVE) is a dataset created to address the problem of detecting audiovisual events in unconstrained videos – both in supervised and weakly supervised settings. The AVE contains twenty-eight event categories distributed over four thousand one hundred forty-three video clips that have been temporally marked at their audio-visual boundaries.
As the name suggests, the BDD100K dataset is an expansive collection of 100,000 driving videos from thousands of hours (more spans) taken from more than 50,000 rides. BDD100K is annotated for 10 different perception tasks in autonomous driving.
Applications of Video Recognition AI
Video recognition technology is everywhere these days. Let’s examine some of its most significant use cases.
- Various industries have successfully implemented video recognition systems in several ways, from detecting objects to monitoring traffic.
- They range from security cameras that recognize objects and human faces to loitering detection systems found in shopping malls and supermarkets to spy devices that can detect changes in normal conditions. Find out the pros and cons of face recognition systems.
- Traffic cameras record moving objects; however, some equipment normally used for other purposes, like traffic lights, may be used to recognize vehicles at the same time.
- Traffic light cameras set at busy road intersections monitor vehicle movement, ensuring no accidents occur along busy roads during peak hours.
Security And Surveillance
Video recognition technology is commonly used in video surveillance to detect objects and events in real time. Such systems can detect suspicious behavior by following along as it happens, notifying security guards about possible dangers, and collecting evidence for criminal cases.
Multiple Instance Learning (MIL) has a slightly different setup from the usual learning situation you are familiar with. Rather than considering each single data point, MIL concerns itself with a collection of data points known as “bags.” These bags are assigned labels such as “positive” or “negative” according to specific criteria; in this case, however, there is a twist: what determines if a bag is considered positive or not depends on whether it contains at least one instance from the positive class (the desired class). That means that as long as a bag has even one positive instance, it may be considered positive.
The problem in MIL is how to develop a model that can correctly decide whether an unknown bag is a positive or negative instance depending on the examples it contains. This is not like ordinary learning, where every single data point gets its own label.
In MIL, you only label the entire bag with may have some positive instances mixed with negatives. The task is, therefore, to spot those bags that contain significant positive examples.
Content Moderation
Content Moderation Platforms such as YouTube and social media networks leverage video recognition technologies to automatically identify unsuitable or harmful materials. This technique can lead to banning or deleting any clip that features violent acts, naked images, or copyrighted stuff, hence upholding community policies and legal requirements.
They can flag, restrict, or remove clips that violate community guidelines by scanning uploaded videos in real time. Such videos may be banned to ensure the safety of users or removed due to copyright concerns. This allows networks to efficiently oversee a huge volume of user-generated content and maintain compliance with local laws and community norms.
Additionally, AI video recognition allows for quicker, more scalable moderation, alleviating the pressure on human moderators and improving enforcement of content consistency. Furthermore, it reduces the amount of exposure such moderators have to disturbing or harmful content. Though imperfect, they are becoming increasingly indispensable in ensuring that online platforms are safer from both a legal angle and socially acceptable standards.
Autonomous Driving
Video recognition is fundamental for autonomous vehicles to successfully perceive and navigate their surroundings. Video recognition tools in autonomous vehicles can detect and track other vehicles, pedestrians, and hurdles, making decisions depending on the detected information.
For instance, a system could notice people walking through a crosswalk in real-time mode and then determine what speed reduction is needed so as not to hit them. It also detects red lights, traffic signs, and line patterns, ensuring it respects driving laws and takes the right direction during the journey. Furthermore, video recognition can help react appropriately under challenging circumstances, including bad traffic situations and road changes that nobody had anticipated due to construction or crashes.
The technology works in conjunction with other systems, such as LiDAR and radar, to gain a full understanding of the environment, enabling it to provide precise and reliable information necessary when driving an autonomous car safely. Furthermore, it is predicted that as video recognition technologies keep improving, they will increasingly help enhance safety among self-driving cars, making them more efficient and able to operate in various driving states.
Retail Customer Behavior Analysis
Video recognition may also provide insight into consumer habits and moods in retail using artificial intelligence. Through security cameras, video recognition tools would keep track of customer movements in stores and interpret their behavior to gather information about their tastes and intentions to buy items, increasing sales volume.
Traffic Monitoring
Traffic monitoring can be supported by video recognition, which provides a real-time analysis of traffic flow. Therefore, estimating traffic volume, analyzing traffic flow, detecting and classifying vehicles, reading license plates of vehicles, detecting pedestrians, and alerting authorities about any incidents can be achieved through this technology.
Video recognition AI can use advanced deep learning techniques without worrying about time, therefore becoming more accurate and efficient when recognizing different events or conditions occurring on roads. Thus, it can change the whole concept of traffic monitoring and improve road safety.
Smart City Management
In smart cities, video recognition technology is increasingly being employed to streamline urban infrastructure and amenities. It accomplishes this by leveraging traffic cameras’ video streams plus those taken by other means within the city, such as public transport facilities or security installations, to control traffic in real-time, managing congestion and detecting accidents as they occur, leading to more effective modes of transportation.
Additionally, the tool is applied during events for crowd monitoring to ensure public safety, detect hazards, and support law enforcement agencies.
It is also used in city planning areas like pedestrian flow analysis or vehicle movement patterns while contributing data that could improve infrastructural development and the delivery of social services in general.
How to Label Videos in V7?
You can efficiently solve video-related computer vision challenges like video recognition using cloud-based solutions such as Google Cloud Vision API and Amazon Rekognition. These models are good at solving general video recognition tasks like sentiment analysis, motion tracking, and landmark recognition.
Nonetheless, when you need a custom solution tailored specifically to your needs, training on your datasets and own model is best. The following tutorial will illustrate using the V7 AI platform for video annotation and model training.
Step 1: Define Your Model’s Inputs and Outputs
- Start by identifying the specific data annotations required for your computer vision task.
- Choose a model type and label class structure that suits your needs (e.g., object detection, segmentation).
- For tasks like video recognition, consider using bounding boxes, which are common for object detection. Alternatively, you may use polygon annotations for training and bounding boxes for predictions.
- In this guide, we’ll use bounding boxes with directional vectors to indicate player head positions.
Step 2: Upload Your Data to V7
- Collect a diverse set of sports videos representing the scenarios your model needs to handle (e.g., different players, teams, lighting conditions).
- Create a V7 account, set up a new dataset, and name it appropriately.
- Drag and drop your training videos into the dataset, and select a suitable frame rate.
- For efficiency, consider preprocessing the videos by extracting frames at a lower frame-per-second (FPS) rate.
- Add classes and choose a workflow type using the default settings to start.
Step 3: Label Your Video Dataset
- Open one of your uploaded videos and select the bounding box annotation tool from the annotation panel.
- Label objects in the video, assigning attributes or classes to represent their state (e.g., directional vectors for player head positions).
- Use the timeline to select keyframes, adjusting labels as needed across multiple frames.
- If manual labeling isn’t preferred, replace the “Annotate” stage with a “Model” stage. You can integrate models from HuggingFace or use public models available on V7.
Step 4: Review Your Annotations
- Review annotations thoroughly, as quality directly impacts model performance.
- Use the Review Stage to identify and correct any annotation errors.
- For larger datasets, delegate annotation and review tasks to different team members to ensure objectivity.
- If reviewing the entire dataset isn’t feasible, connect a sampling stage to review a subset of the annotations while maintaining quality control.
Step 5: Train Your Video Recognition Model
- With your dataset labeled, proceed to train your model. You have several options:
- Scenario A: Use V7’s cloud models panel to train and deploy a video recognition model quickly.
- Scenario B: Register an external model through the panel and connect your data using V7’s REST API.
- Scenario C: Export annotations as JSON files and use them with your preferred machine-learning architecture for more complex use cases.
Step 6: Evaluate Your Model’s Performance
- Train multiple versions of your model iteratively to enhance its performance.
- Use a Consensus Stage to compare outputs from different models and analyze the overlap.
- Continuously improve by providing more high-quality annotated data.
For more information, consult V7’s documentation and explore the platform’s features to tailor your video recognition software. Consider booking a demo to see the platform in action.
Best APIs for Seamless Video Recognition Integration
Google Video Intelligence API: has a broad selection of features that can be used to detect objects within videos.
Amazon Rekognition: offers many pre-trained models as well as tools that are used to train models independently.
Microsoft Image Processing API: Contains numerous user-friendly algorithms for detecting objects in videos.
Wrapping Up
Video recognition has become an important technology that has changed many professions by automatically analyzing large amounts of video data. It can be used for security purposes, shopping without cashiers, and traffic flow management, among others.
Even though there is a problem with not having enough well-categorized databases upon which traditional methods depend, modern approaches like neural networks and image processing provide better accuracy levels when dealing with video recognition tasks despite low latency requirements. As advancements are made in these areas, it will be possible to improve upon them so that business operations become much more efficient and safe through automation.
Are you ready to learn more about AI video recognition? We can help your business grow using this new-age technology, from enhancing safety across your premises to increasing automation processes within it. Reach out, and we will help you leverage video recognition features to take your business to the next level and make it better than ever.