Description:The system consists of the following components:
1. Stream Capture Device: This device captures video streams from CCTV cameras.
2. Stream Processor: This device pre-processes the video streams, such as by decoding them from H.264 format.
3. DNN Architecture: This is the deep neural network architecture that is used to detect suspicious activities in the video streams. The DNN architecture consists of configurable CNN layers that can be trained on a dataset of images and videos that contain examples of suspicious activities.
4. Data Storage Unit: This unit stores the video streams and the results of the DNN analysis.
5. Decision Maker: This component makes decisions about whether or not to send an alert based on the results of the DNN analysis.
6. Client Devices/Services: These are the devices or services that receive the alerts from the system.

The system works as follows:
1. The Stream Capture Device captures video streams from CCTV cameras.
2. The Stream Processor pre-processes the video streams.
3. The pre-processed video streams are fed into the DNN architecture.
4. The DNN architecture analyses the video streams and identifies any suspicious activities.
5. The results of the DNN analysis are stored in the Data Storage Unit.
6. The Decision Maker makes a decision about whether or not to send an alert based on the results of the DNN analysis.
7. If the Decision Maker decides to send an alert, it sends the alert to the Client Devices/Services.
The system can be used to detect a variety of suspicious activities, such as the presence of sharp objects or weapons, unattended packages, and people loitering in restricted areas. , Claims:We Claim:
Claim1: A video surveillance system, comprising:
• a stream capture device configured to capture video streams from CCTV cameras;
• a stream processor configured to pre-process the video streams by decoding them from an H.264 format;
• a deep neural network architecture comprising configurable convolutional neural network layers trained on a dataset of images and videos containing examples of suspicious activities, the architecture configured to analyse the pre-processed video streams and identify suspicious activities including the presence of sharp objects or weapons;
• a data storage unit configured to store the video streams and the results of the analysis;
• a decision maker configured to make decisions about whether or not to send an alert based on the results of the analysis; and
• client devices or services configured to receive the alerts from the decision maker.

Claim 2: In addition to claim 1, wherein the convolutional neural network layers are further configured to differentiate between sharp objects and harmless objects, thereby reducing false alarms
Claim 3: In addition to claim 2, wherein the system is further configured to process video data in real-time, enabling immediate identification and notification of potential threats.
Claim 4: In addition to claim 3, wherein the system is further configured to be adaptable to different environments and security requirements by adjusting the parameters of the deep neural network architecture.