The main purpose of our project is to increase security by recognizing people and tracking them if they are unknown. Because in our daily lives, only standard security cameras are used, which have no record of any person, known or unknown, who enters a location. Previously, the record was kept by ha
Vision based human tracking and localization for security surveillance
The main purpose of our project is to increase security by recognizing people and tracking them if they are unknown. Because in our daily lives, only standard security cameras are used, which have no record of any person, known or unknown, who enters a location. Previously, the record was kept by handwriting. In contrast, in our project, we can easily keep a record (entry and exit time with image) of an unknown person as well as track an unknown person.
The main purpose of our project is to fulfill the security need which acquire less human effort and provide high level security. Our system will recognize all people and will track the unknown persons without any external help and also maintain the record of every person which enters in our society. Our system will have all the data like at which time the unknown person was present in our society. If any unknown person enters in our society or university for many times then an alert will appear to authority and if we want to know whether or not a person has ever entered our society, we can readily verify using our system. If any criminal is hiding in our society we can locate his position by seeing his track.
1. Identify the persons entering the site premises
2. Maintain the record of entrance of both known and unknown persons
3. Track and locate the position of the unknown persons in the project site.
First of all, we will use cameras for making real time video of different location points. Then we process the videos using Machine learning and Deep Learning algorithms. First we will apply the machine learning algorithm to detect the human in the video. And then we’ll use the algorithm to detect the face of person. Then we apply the deep learning to recognize the person’s face. After recognizing the face, we will check from our dataset that it is present in our record or not. If record founded it means it is known person otherwise it is unknown person. If it is unknown person, then we label that person and after that tracking will start. In tracking when the person comes in front of next camera, again detection and recognition process takes place and checked that person is labeled or not. If it’s labeled, the record will update (camera no, and time will add in his record).
The benefit of this project is as follow:
The technologies which we use in this project are given below:
First of all, we train the model to recognize the known persons using the TensorFlow library on GPU. We take real-time video using cameras that are directly attached to the computer system. GPU process these images of the video and classify the known and unknown persons. After recognizing the person it decides either to send the mail or not to authorities. When this procedure has been done the system sends the signal to the IoT processor to open the door. Which door will open is decided on the bases of the signal sent to the IoT processor.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Graphic Card | Equipment | 1 | 40000 | 40000 |
| camera | Equipment | 5 | 1600 | 8000 |
| ESP 32 | Equipment | 1 | 1000 | 1000 |
| data cable | Equipment | 1 | 200 | 200 |
| card board | Miscellaneous | 1 | 1000 | 1000 |
| Saw | Miscellaneous | 1 | 200 | 200 |
| convertor | Equipment | 1 | 200 | 200 |
| 4 in 1 USB connector | Equipment | 1 | 400 | 400 |
| servo motor | Equipment | 5 | 270 | 1350 |
| flex print | Miscellaneous | 1 | 500 | 500 |
| liquid Glue | Miscellaneous | 10 | 20 | 200 |
| Board Marker | Miscellaneous | 2 | 50 | 100 |
| Total in (Rs) | 53150 |
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