I build a model which can detect custom objects from CCTV footage. Where my model can detect three types of objects from the footage. But I want to detect anomalies by the model from the footage.
For training purposes, I just only use normal (which are not
anomalies) videos and my model should detect anything abnormal that
happens.
Related
I have been using roboflow to annotate door and door handle images, (about 1300 images).
For generating the model, I have used auto-orientation and resizing to 416*416 to pre-process
and vertical flip for augmentation.
After testing the model on a webcam and seeing the test set predictions, the model seemed to predict doors as door handles and door handles as doors almost in every image.
In a nutshell, the issue is that the model predicts the bonding boxes accurately however have all the labels switched.
I have also tried using the yolov5 github code and it has the same issue.
I can not pin point where is the problem. I would appreciate any help in this matter and any tips to prevent this from happening.
I'm developing an object tracking system using Google's Vertex AI AutoML Video Tracking. We currently have an accurate model that identifies objects per frame (as a picture) and I'm exploring models that may be able to gain further insight and accuracy by using a collection of frames (video) for the classification and tracking purposes. I want to learn more about the architecture used in the AutoML Object Tracking, but all I can find is articles hyping up the dynamic nature of the architecture. Mainly, I'm trying to answer the following 3 questions:
What methods does the AutoML Object Tracking use to classify the objects and track them? Are the classifications done frame to frame, with a Euclidean distance tracker mapping objects together? Or are the objects identified and classified across multiple frames a recurrent network in space (image) and time (frame to frame). Something like a LSTM.
What performance can object tracking in AutoML achieve that is better than their image object identification models?
Where can I go to learn more about the model architectures on Vertex AI? It's hard to know which google publications are associated with their current platform.
Any feedback is greatly appreciated!!!
Is it possible to use GCP Machine Learning products to train a model to draw bounding boxes on certain objects in an image? I'd like to be able to feed labeled images and have it predict where that label would belong.
I think you are looking for something like this, where the Tensorflow machine learning library is used:
https://cloud.google.com/solutions/creating-object-detection-application-tensorflow
A note:
When you say that you want to be able to feed labeled images and have it predict where that label would belong, i assume you mean where that object is present in the image in terms of the bounding box coordinates. If so then the library should take care of that for you, your job is just to train the network with your labeled images.
I have a task where i am asked to track parcels(carton boxes) of different dimensions moving on a conveyor
I am using Asus Xtion pro camera and also i have been asked to use Point cloud data to detect & track the object on the conveyor
I have read so many model-based methods where we need to create a model of the object to be detected and then we perform keypoints extraction,feature mapping and so many other concepts between the scene and the model.
Since i am using Boxes of different dimensions, i definitely need a model of each of them to match with the scene.
My question : Can i have one common point cloud model of a box of some dimension and compare it with any box that comes under the view of a camera? I meant can i have one scalable model to compare with box of any dimension? is it possible?
My chief doesn't want the project to be dependent on many models for detection and tracking. One common model which is scalable or parameterized should do the trick it seems.
Thanks in advance
In a computer vision project, the image I want to process can be partitioned in "zones" containining multiple products of the same kind.
Provided that I can retrieve image information of all the possible kinds of product, I need to detect which kind is present in each zone, without the need to detect the position of each single product. In summary, I need to recognize "sets of products".
As additional info, the products have not a rigid shape, they are not oriented in the same manner and luminosity changes (so I am basically searching for shape, orientation and luminosity invariant approaches).
The reliable info I can exploit is that the products logos - or parts of them - are often visible and the products are quite colorful.
I would like to know about possible approaches that exploit the fact that I know the zones partition and approaches that do not exploit it.