Dear Aarohi Your channel is very knowledgeable & helpful for all Artificial Intelligence/ Data Scientist Professionals. Stay blessed & keep sharing such a good content.
@@CodeWithAarohi need a help i need to track a poor video quality video lost of variation in it i try deep sort but how can i enhance it more any though about clustring and anything ??
Hello Aarohi Your channel is very knowledgeable & helpful for all Artificial Intelligence/ Data Scientist Professionals. Stay blessed & keep sharing such a good content. Your channel really needs more likes & share so to reach maximum AI professionals who can encash from it
Hello Ma’am Your AI and Data Science content is consistently impressive! Thanks for making complex concepts so accessible. Keep up the great work! 🚀 #ArtificialIntelligence #DataScience #ImpressiveContent 👏👍
Hi Aarohi, your content is excellent and your channel is one of the best Artificial Intelligence channel but still not getting that much of likes which your channel deserves. Hope you succeed #AI #ArtificialIntelligence #DataScience #EducationalContent
What I observe is You are first detecting and then incrementing the counter actually, the counting should be solely dependent on each frame not on incrementing a new object every frame caz the object detection model can generate some false detections as well. and the counter may increment. and the actual people in a video are less but the counter can be greater .
That was amazing work, madame! ❤️ I just have a few questions. What modifications are needed in this code to count multiple objects, not just people? Also, how can I make it count in real-time? Thanks for answering!
If you will notice in github repo. I have this line where I have mentioned the classes = 0 which means only person class. Remove "classes = 0" from this line and it will detect and track all objects. results = model(frame, device=0, classes=0, conf=0.8)
Hello ma'am, can object tracking and counting methods like deepsort only be used for video input? what about image input? can it still be used? and how effective is it?
Hai mam. I have a yolov8 detection model for my dataset. in that i have five classes. i need to track and count the defective product and i need to reject that defective product from conveyor belt. so for that i need to create a text file with all defective product is assigned as 0 and defect-free product as 1. how to do that.
Hi Ma'am, I'm back with another issue. My code executed successfully and an output video was created, but the video file is 0 KB and cannot be opened. Could you please help me resolve this? ( Btw I use Google colab) Thank you!
Hi. Thank you for your great channel and so interesting resources. Maybe it sounds like a silly question, but where is the folder with the pictures you use in this video. Regards Rafael
U didnt teach any theory about yolov8 's architecture loss function how it works ext... Directly going to code is not a big think we too get plenty of codes from net we finding difficult to get the theory behind yolov8 plz focus on that. This is my humble request.
Madam, From where did you get that deepsort files from ? can you please provide me the resources. i know i can clone the deepsort folder directly from your git account but i need the source from which you had downloaded. Thank you in advance Madam.
Maam I am unable to find the "yolov8_DeepSORT" as when I clone the git repo it gives me the "deep_sort" folder but nothing else. Please help me if I am missing something as the code is unable to find the the images and giving me a "file not found" error
why haven't you used bytetrack? and which is better yolov8 tracker, deepsort or bytesort as there is alot of object detection in the video and I am using yolo v8 detector trained on custom dataset.
Hello, thank you very much for the video, it was really helpful. However, I am running my code on Jupyter Notebook and I am receiving a 'dead kernel in Jupyter' error. What should I do?
You can restart the kernel- try "Kernel" > "Restart & Clear Output" If the above solution doesn't work then Close the Jupyter Notebook tab in your web browser, stop the Jupyter Notebook server (you can do this in the terminal/command prompt where you started it), and then restart the Jupyter Notebook. Excessive memory usage in your code can also lead to a dead kernel.
Hi, ma'am , I have my costumed trained model of Yolov8 using CLI how can we use DEEP SORT for tracking purpose in CLI ? I mean how can we use deep sort algorithm using CLI ?
I am working on a project that is sapling counting using a drone. I am using Yolo V8 for object detection. The issue I am facing is that when the drone takes a U-Turn to the next area of the field basically other lines of saplings the previous saplings are counted again in that camera rotation. Can you suggest any idea how to figure this out? I want to do something that will help to stop that extra counting of that sapling while the drone is taking a U-Turn to change its lap.
According to me, this problem can be solved by these steps: You can pick detections only from the center of the image to reduce the chances of counting the same objects when the drone changes its perspective. 1- Crop the input image to retain only the central region. The size of the central region can be adjusted based on your requirements. 2- Perform object detection on the cropped central region of the image using YOLOv8 or your chosen detection model. This will focus the detection on the area of interest. 3- Implement object tracking within the cropped central region. Assign unique IDs to detected objects and track them within this limited area. 4- Count objects based on their unique IDs within the central region. When the drone changes its perspective or takes a U-Turn, you'll still be counting objects within the central region, which should help prevent double-counting. Keep in mind that the size of the central region you choose should be a balance between ensuring accurate object counting and maintaining a wide enough field of view to capture relevant objects. Experimentation and testing will be key to determining the optimal size for your specific application.
Calculate the center point of the image and compare the horizontal positions of each detected person's bounding box relative to this center point. This will help you determine which person is on the left and which one is on the right. Then filter out all persons except the one on the left and the one on the right.
Instead of processing every frame in the stream, process every 2nd or 3rd frame to reduce the computation load. This helps to catch up with real-time streaming. Also, Use lower resolution frames to reduce the computation load. You can downsample frames and still maintain reasonable detection performance. YOu can try to work with ByteTrack which is lighter than DeepSORT.
Dear Aarohi
Your channel is very knowledgeable & helpful for all Artificial Intelligence/ Data Scientist Professionals. Stay blessed & keep sharing such a good content.
Thanks a lot
Thank you for your video ma'am. it's straight to the point. Please have a great day!!
You're very welcome! I'm glad you found the video helpful and to the point. Have a fantastic day too!
@@CodeWithAarohi need a help i need to track a poor video quality video lost of variation in it i try deep sort but how can i enhance it more any though about clustring and anything ??
Hello Aarohi
Your channel is very knowledgeable & helpful for all Artificial Intelligence/ Data Scientist Professionals. Stay blessed & keep sharing such a good content. Your channel really needs more likes & share so to reach maximum AI professionals who can encash from it
Thank you, I will
Amazing work done. Keep it up
Thank you!
Hello Ma’am
Your AI and Data Science content is consistently impressive! Thanks for making complex concepts so accessible. Keep up the great work! 🚀 #ArtificialIntelligence #DataScience #ImpressiveContent 👏👍
My pleasure 😊
Hi Aarohi, your content is excellent and your channel is one of the best Artificial Intelligence channel but still not getting that much of likes which your channel deserves. Hope you succeed #AI
#ArtificialIntelligence
#DataScience
#EducationalContent
Thank you so much for your kind words and support! It means a lot to me. 😊🙏
What I observe is You are first detecting and then incrementing the counter actually, the counting should be solely dependent on each frame not on incrementing a new object every frame caz the object detection model can generate some false detections as well. and the counter may increment. and the actual people in a video are less but the counter can be greater .
That was amazing work, madame! ❤️ I just have a few questions. What modifications are needed in this code to count multiple objects, not just people? Also, how can I make it count in real-time? Thanks for answering!
If you will notice in github repo. I have this line where I have mentioned the classes = 0 which means only person class. Remove "classes = 0" from this line and it will detect and track all objects.
results = model(frame, device=0, classes=0, conf=0.8)
@@CodeWithAarohi thanks for much for answering ❤️ and what i need to modify to count in real time not only in videos?
Nice Job, is there a way to implement with Home Assistant?
Explained in a very nice manner, thank you man
Glad it helped!
Maam how to do class wise counting ?
Worth watching again ma'am
Hello ma'am, can object tracking and counting methods like deepsort only be used for video input? what about image input? can it still be used? and how effective is it?
up
Hai mam. I have a yolov8 detection model for my dataset. in that i have five classes. i need to track and count the defective product and i need to reject that defective product from conveyor belt. so for that i need to create a text file with all defective product is assigned as 0 and defect-free product as 1. how to do that.
Hi Ma'am,
I'm back with another issue. My code executed successfully and an output video was created, but the video file is 0 KB and cannot be opened. Could you please help me resolve this? ( Btw I use Google colab)
Thank you!
Hi.
Thank you for your great channel and so interesting resources. Maybe it sounds like a silly question, but where is the folder with the pictures you use in this video.
Regards
Rafael
That I didn't upload but you can create a folder and put some images in it and test.
Very well explained
Keep watching
Hi Ma'am, Can you explain more about the deep_sort_weight?
U didnt teach any theory about yolov8 's architecture loss function how it works ext... Directly going to code is not a big think we too get plenty of codes from net we finding difficult to get the theory behind yolov8 plz focus on that. This is my humble request.
Will cover that in a separate video.
@@CodeWithAarohi tq very much💖 I am waiting
Madam, From where did you get that deepsort files from ? can you please provide me the resources. i know i can clone the deepsort folder directly from your git account but i need the source from which you had downloaded. Thank you in advance Madam.
I picked that code long time ago from github. Not sure which repo. Sorry
Very nice video
Thanks
Can you please share the path from where i can download this video and photo.
Maam I am unable to find the "yolov8_DeepSORT" as when I clone the git repo it gives me the "deep_sort" folder but nothing else.
Please help me if I am missing something as the code is unable to find the the images and giving me a "file not found" error
if anyone can help me with this problem
Email me at aarohisingla1987@gmail.com I will send you the code
why haven't you used bytetrack?
and which is better yolov8 tracker, deepsort or bytesort as there is alot of object detection in the video and I am using yolo v8 detector trained on custom dataset.
No reason. This topic is requested by somebody, so did a video on it but try to work with bytetrack
Hi thanks for the video. One question, can I use yolo-nas instead of yolov8?
Yes you can!
Hello, thank you very much for the video, it was really helpful. However, I am running my code on Jupyter Notebook and I am receiving a 'dead kernel in Jupyter' error. What should I do?
You can restart the kernel- try "Kernel" > "Restart & Clear Output"
If the above solution doesn't work then Close the Jupyter Notebook tab in your web browser, stop the Jupyter Notebook server (you can do this in the terminal/command prompt where you started it), and then restart the Jupyter Notebook.
Excessive memory usage in your code can also lead to a dead kernel.
Hi, ma'am , I have my costumed trained model of Yolov8 using CLI how can we use DEEP SORT for tracking purpose in CLI ? I mean how can we use deep sort algorithm using CLI ?
You can create a .py file and write a code to call deepsort algorithm there. And then run that file from cmd.
Thank You ma'am@@CodeWithAarohi
Hello ty for the content
Welcome!
Thanks dear I will test it
Most welcome 😊
There is any your repo ya video for person re-identification
No, I don't think that I have posted any video on reid.
Nice.
Thanks!
I am working on a project that is sapling counting using a drone. I am using Yolo V8 for object detection. The issue I am facing is that when the drone takes a U-Turn to the next area of the field basically other lines of saplings the previous saplings are counted again in that camera rotation. Can you suggest any idea how to figure this out? I want to do something that will help to stop that extra counting of that sapling while the drone is taking a U-Turn to change its lap.
According to me, this problem can be solved by these steps:
You can pick detections only from the center of the image to reduce the chances of counting the same objects when the drone changes its perspective.
1- Crop the input image to retain only the central region. The size of the central region can be adjusted based on your requirements.
2- Perform object detection on the cropped central region of the image using YOLOv8 or your chosen detection model. This will focus the detection on the area of interest.
3- Implement object tracking within the cropped central region. Assign unique IDs to detected objects and track them within this limited area.
4- Count objects based on their unique IDs within the central region. When the drone changes its perspective or takes a U-Turn, you'll still be counting objects within the central region, which should help prevent double-counting.
Keep in mind that the size of the central region you choose should be a balance between ensuring accurate object counting and maintaining a wide enough field of view to capture relevant objects. Experimentation and testing will be key to determining the optimal size for your specific application.
@@CodeWithAarohi thank you
how to convert these into cuda tensors please update mam
One doubt shall I use yolov8 for skin lesion counting and detection purpose?
Yes, you can use. I recently did this video where I am detecting, tracking and counting using yolov8 : ruclips.net/video/Z3uquMElyzI/видео.html
reidentification??????????? without it that code did not do good for my project
hi mam . after processing few frAMES ITS GETTING DEAD . I AM USING AWS PYTORCH KERNEL .
Check if you are running out of resources
Could you please share the pre-trained model!
which model?
Hi ma’am! How did you get the class name of the track id?
I am getting it from detection model
@@CodeWithAarohihow so? At 7:04, it can be seen that the arguments passed to tracker are only bboxes_xwyh, conf, og frame?
I want only for "person" detection so what should i do some changes in this code??
just write classes = 0 where you are calling the model
How can we apply to detect n count objects in images ? kindly guide me
Just provide the source image instead of video.
Ma'am, how can we change the input to real time and show the real time results in another window real time?
Use source as 0 if you want to use webcam, And if you want to use other source then provide that in source.
video is created but there is no data :- code executed successfully and output video also created but it has 0kb data and can't open.
Can it be done for real time , like using our own webcam for counting ?
Yes
if we want to only track the person at left and the person at right, what should we do?
Calculate the center point of the image and compare the horizontal positions of each detected person's bounding box relative to this center point. This will help you determine which person is on the left and which one is on the right.
Then filter out all persons except the one on the left and the one on the right.
%cd yolov8_DeepSORT Mam where is this folder located?
github.com/AarohiSingla/Tracking-and-counting-Using-YOLOv8-and-DeepSORT
i am working in google colab but cant get the output video
i see that it is not releasing after the loop is done
Facing the same issue. Found any solutions ?
I also work at the collab. Did you manage to run the code? I'm having problems importing the DeepSORT method. How did you handle it?
does deepsort really need a dedicated gpu?
No, You can work with cpu also but it will be slow as compare to gpu.
How to handle live stream because deepsort create delay in processing and could create overall delay in output
Instead of processing every frame in the stream, process every 2nd or 3rd frame to reduce the computation load. This helps to catch up with real-time streaming.
Also, Use lower resolution frames to reduce the computation load. You can downsample frames and still maintain reasonable detection performance.
YOu can try to work with ByteTrack which is lighter than DeepSORT.
Have you any sample code for byte track
Thank you very much
@@jagdishlamba72 ruclips.net/video/Jkf9t6jG3vg/видео.html
Mam images/person.jpg is not present in github
Create a folder with a name of " images" and then put image inside it.
which software r u using to record ur videis
obs studio
Maam can we make it real time
Yes
video is created but there is no data :- code executed successfully and output video also created but it has 0kb data and can't open.