Best solution for data storage problem in CCTV

This blog is to help data storage problem in CCTV cameras
Best solution for data storage problem in CCTV

Because a great amount of data is generated in today's modern world, retrieving the essential information from large data volumes is challenging, and storing high data volumes is a problem too. The transmission and storage of massive data volumes in the form of frames and images is a part of image and video processing. Several have researched on lowering bandwidth requirements, while others focused on improving storage efficiency.

For security purposes, many techniques for motion detection have been developed. MATLAB, an external motion sensor, a genetic algorithm, motion-based storage optimization, and others. However, each had its own problems, such as greater complexity and delayed frame processing.

Here are two methods that may be utilized with existing cameras and are rather uncomplicated to process.

1. Adaptive Thresholding Motion Detection for Storage Optimization

Because it only retains the informative frames, this motion detection technique requires fewer calculations and decreases the required data storage by up to 70%, allowing security staff to obtain the relevant information more rapidly. Also, the suggested method uses a histogram-based adaptive threshold for motion detection, it can be used in a variety of lighting circumstances.

Method used in Adaptive Thresholding Motion Detection for Storage Optimization
Method used in Adaptive Thresholding Motion Detection for Storage Optimization

  • For processing, the red channel is used:

When the colour of the scene isn't important, an RGB frame can be converted to grayscale. Two additions and one assignment per pixel are required for the simplest RGB to grayscale conversion, but advanced conversion requires multiple arithmetic operations.

Furthermore, instead of using the red channel, the green channel might be used. However, human skin and natural luminance contain fewer blue channel contributions, utilising the blue channel may not produce satisfactory results.

  • Calculation of Adaptive Thresholds:

Due to changing atmospheric conditions and lighting, the luminance of a scene collected by a CCTV camera fluctuates throughout the day. When a CCTV camera records video at 30 frames per second, it takes slightly less than six minutes to capture a ten thousand frame film, however, when 60 frames per second are used, the time is decreased to three minutes. The next threshold is calculated using the first frame after every ten thousand.

Each frame with motion is saved in the database, while the rest is ignored.

The picture below depicts a test instance taken at night, including an original frame, a greyscaled frame, and the histogram of the red channel.

Daytime test (1) Original colored image, (2) grayscaled  image
Daytime test (1) Original colored image, (2) grayscaled image

Night time test (1) Original colored image, (2) grayscaled  image
Night time test (1) Original colored image, (2) grayscaled image

  • Results –

Significant optimization was found when the above strategy was applied to CCTV camera stored footage. Because the CCTV footage exhibited limited motion, a film with a running time of 268.741 minutes was cut down to 32.25 minutes.

The overall frame storage was lowered by 88.4% in percentage terms. Due to the utilisation of a single colour channel for motion detection, the proposed method outperformed conventional methods by saving several computations.

2. Smart Detector

Everyone has a CCTV camera installed for security reasons. The incidents are continuously recorded by CCTV cameras. As a result, if nothing is happening in front of the camera, there is needless storage waste. A person is assigned to monitor cameras 24 hours a day, seven days a week. This smart detector will record each and every move, and if any strange activity is detected in front of the camera, it will automatically notify the appropriate person.

Block diagram for Smart Detector
Block diagram for Smart Detector

  • Real-Time Security System with Human Motion Tracking and Identification

The practice of separating foreground objects from the background in a series of video frames is known as background subtraction. The surveillance system's main goal is to detect and follow a moving object employing a single camera.

  • Use of SOS application

It will allow users to immediately notify the cops in the event of a sexual assault, kidnapping, or "eve-teasing," as well as natural calamities such as earthquakes or floods. It can also be utilized in situations where the user is unexpectedly stuck, such as in an elevator, and is unable to contact anyone.

  • STIP Techniques for Human Action Identification

The Human Visual System is the most well-built image processing system that combines the human eye and the brain. The attempt is to construct a computer vision system using this database. The approach of labelling all related contents of a single data set is referred to as supervised algorithm.

Python is used as the Programming language. YOLO v3 architecture is used for object detection

And Deep sort architecture is used for object tracking

These systems can be used in a variety of contexts, including banks, traffic, hospitals, and public spaces. It will also save time and minimise manpower.

The forecast for 2025 values the global video surveillance market at nearly three times the size as it was in 2016

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