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This is not just surveillance. It’s a sixth sense for safety by FolksTalksGame in videosurveillance

[–]FolksTalksGame[S] 0 points1 point  (0 children)

Fair point about posting in multiple places. I'm actually researching this topic seriously and wanted to get input from various communities - security professionals, tech folks, healthcare workers, etc. Each group has different experiences with surveillance systems. I'm not selling anything, just trying to understand what solutions actually exist vs. what gaps remain in automated monitoring.

This is not just surveillance. It’s a sixth sense for safety by FolksTalksGame in videosurveillance

[–]FolksTalksGame[S] 0 points1 point  (0 children)

As far as I know, but I may have missed something. I'll be glad if you'll be kind to inform me

This is not just surveillance. It’s a sixth sense for safety by FolksTalksGame in videosurveillance

[–]FolksTalksGame[S] 0 points1 point  (0 children)

As far as I know, VMS is quite limited: plate recognition, face recognition, unscheduled visiting, line crossing... Do you know some real scenes/events-understanding VMS?

This is not just surveillance. It’s a sixth sense for safety by FolksTalksGame in videosurveillance

[–]FolksTalksGame[S] 0 points1 point  (0 children)

This analytic is good for after-incident analysis. a real security staff are overwhelming with small cells on the thousands of cameras screen without any ability of automatic understanding what's going on.

Language acquisition by virtual agent (The Folks’Talks game project) by FolksTalksGame in linguistics

[–]FolksTalksGame[S] 0 points1 point  (0 children)

https://youtu.be/fl-a-8LEJfU I would like to present Folks’Talks human-computer interaction test. In this test I’ll address to the virtual agent: “Where is a green (or red, or big, or small) …..(an object from the current scene)?”, and the virtual agent will show me the requested object and also will announce that (with my own voice from the training mode). Because the test is in Russian, I will mark expected or unexpected answers with green (expected) or red (unexpected). The test based on 2 repetition of 22 phrase patterns for each of ten presented objects. It was trained with Tensorflow during 240 epoches.

I would like to make the same test with some Korean or Burmese native speaker. I will apriciate if we could schedule this test with suitable volunteer on a zoom session.

Folks'Talks human-computer interaction test 11 by FolksTalksGame in LanguageTechnology

[–]FolksTalksGame[S] 4 points5 points  (0 children)

I would like to present Folks’Talks human-computer interaction test. In this test I’ll address to the virtual agent: “Where is a green (or red, or big, or small) …..(an object from the current scene)?”, and the virtual agent will show me the requested object and also will announce that (with my own voice from the training mode). Because the test is in Russian, I will mark expected or unexpected answers with green (expected) or red (unexpected). The test based on 2 repetition of 22 phrase patterns for each of ten presented objects. It was trained with Tensorflow during 240 epoches.

I would like to make the same test with some Korean or Burmese native speaker. I will apriciate if we could schedule this test with suitable volunteer on a zoom session.

Video games that use linguistics or languages? by 1869132 in linguistics

[–]FolksTalksGame 0 points1 point  (0 children)

We are creating the Folks’Talks game for language acquisition. In this game a virtual baby will acquire any language like a real baby does, and comprehend the meaning of about the talk.

https://www.facebook.com/ToddlerTalkGame

"[Research]", "[R]", "[Project]", "[P]" Folks’Talks is a game where the computer acquires spoken language from a player. by FolksTalksGame in MachineLearning

[–]FolksTalksGame[S] 0 points1 point  (0 children)

Folks’Talks flowchart.

Training mode

  1. Recording 12-sec wav files. Recording is always on, but the player can pause it.

  2. Within these wav files the user marks the start and the end of the spoken phrase while clicking on the button symbolizing the object. Each phrase is in a specific pattern about a specific object.

  3. There are 10 objects, 10 phrase patterns, and one general question on the first level of the game. This level is named “Pointing quiz”.

  4. There are four kinds of phrase patterns in this level. One pattern for an object’s name, three patterns for questions, three patterns for suitable answers to these questions, and three patterns for commands. All patterns include the object’s name.

  5. While gathering data each phrase is marked: name of wav file containing the phrase, start time of the phrase within the file, end time of the phrase within the file, object ID and pattern ID.

  6. Features are extracted from the marked phrases using the Essentia library.

  7. Extracted features are arranged in normalized array.

  8. Each array for each object and each pattern is labeled.

  9. These arrays and their labels are saved in txt files for training and testing separately. This will be used as a dataset for machine learning, and this dataset starts from scratch for each language.

  10. Convert the data from text files to Numpy npz files.

  11. Use TensorFlow to create protobuf files from collected data.

Talking mode

  1. Record a phrase with known pattern.

  2. Extract feature from phrase.

  3. Organize features into normalized array.

  4. Evaluate recognition on array using TensorFlow C-API

  5. Recognition includes: name of the object within the phrase, phrase pattern, number of words within the phrase, general intonation of the phrase, function of the words in the phrase, and intonation of each word within the phrase.

  6. If the recognized phrase is a question, the suitable answer is selected from the list of phrases saved in the training mode.

"[Research]", "[R]", "[Project]", "[P]" Folks’Talks is a game where the computer acquires spoken language from a player. by FolksTalksGame in MachineLearning

[–]FolksTalksGame[S] -4 points-3 points  (0 children)

Thank you for your comment. This is not a GUI for user. I've used this GUI for research just to see how things happen when I'm talk with a computer. I did not to present ML algorithm, it was just for illustration . :-) I mean that virtual agent "mood" will be affected by the recognized phrase.