# If anyone has worked on multi-camera person tracking, MTMCT, Person Re-ID, retail analytics, or CCTV analytics, I would really appreciate suggestions, papers, architectures, repositories, or practical implementation advice

**URL:** <https://discuss.roboflow.com/t/if-anyone-has-worked-on-multi-camera-person-tracking-mtmct-person-re-id-retail-analytics-or-cctv-analytics-i-would-really-appreciate-suggestions-papers-architectures-repositories-or-practical-implementation-advice/12435>\
**Category:** 🤝  Community Help\
**Created:** [August 19, 2026, 7:09pm UTC](https://discuss.roboflow.com/t/if-anyone-has-worked-on-multi-camera-person-tracking-mtmct-person-re-id-retail-analytics-or-cctv-analytics-i-would-really-appreciate-suggestions-papers-architectures-repositories-or-practical-implementation-advice/12435 "2026-08-19T19:09:03Z")\
**Posts on this page:** 2\
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**Author:** ![Pasindu\_Thennakoon](https://yyz1.discourse-cdn.com/flex029/user_avatar/discuss.roboflow.com/pasindu_thennakoon/32/9783_2.png) [@Pasindu\_Thennakoon](https://discuss.roboflow.com/u/Pasindu_Thennakoon)\
**Post date:** [August 19, 2026, 7:09pm UTC](https://discuss.roboflow.com/t/if-anyone-has-worked-on-multi-camera-person-tracking-mtmct-person-re-id-retail-analytics-or-cctv-analytics-i-would-really-appreciate-suggestions-papers-architectures-repositories-or-practical-implementation-advice/12435/1 "2026-08-19T19:09:03Z")

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I’m currently developing a **real-time multi-camera person tracking and re-identification system for a retail shop** , and I’m facing some identity consistency issues. I would really appreciate some advice from people who have experience with Person Re-ID or Multi-Camera Multi-Target Tracking (MTMCT).

### My goal

I have multiple CCTV cameras in a retail environment, and I want to:

- Detect and track multiple people in real time.

- Assign each person a **Global ID**.

- Keep the **same Global ID when the same person appears on another camera**.

- Avoid assigning the same Global ID to different people.

### Current Re-ID setup

For Person Re-Identification, I am using:

- **OSNet x1.0**

- A custom-trained model based on the **Market1501 dataset**

- Person embeddings extracted from detected person crops

- Cosine similarity to compare new embeddings with previously stored embeddings

- A global gallery/database containing embeddings belonging to each Global ID

The basic workflow is:

**Camera → Person Detection → Local Tracking → Person Crop → OSNet Embedding → Compare with Global Gallery → Assign Existing Global ID or Create New Global ID**

### The problem

The system works, but I am getting two major errors:

1. **Same person → Different Global IDs**

For example:

`Person A → Camera 1 → ID 5`

Later:

`Person A → Camera 2 → ID 12`

Even though it is the same person.

1. **Different persons → Same Global ID**

For example:

`Person A → ID 5`

Then another person with somewhat similar clothing:

`Person B → ID 5`

This is much more problematic because two different people become merged into one identity.

The retail environment also contains real-world challenges such as:

- Occlusion

- Similar clothing

- Different camera angles

- Different lighting conditions

- Partial-body detections

- People entering/leaving the camera view

- Large pose changes between cameras

- Overlapping camera views

### What I would like help with

I would appreciate guidance on designing a more reliable **real-time multi-camera Re-ID architecture**.

In particular:

- Is **OSNet x1.0 trained/fine-tuned on Market1501** suitable for a retail CCTV environment?

- Should I fine-tune OSNet using images collected from my own cameras?

- What is the best strategy for choosing a cosine similarity threshold?

- Is comparing against a single/average embedding per Global ID enough, or should I maintain multiple embeddings for every identity?

- How can I prevent incorrect embeddings from contaminating a person’s global gallery?

- Should Global ID assignment require multiple matching embeddings instead of one similarity match?

- How should I handle people wearing very similar clothes?

- Should camera topology, timestamps, entry/exit zones, and travel time between cameras be included in Global ID matching?

- Would approaches such as **FastReID, TransReID, CLIP-based Re-ID, or another modern Re-ID model** provide a significant improvement?

- What is the recommended architecture for combining the local tracker and global Re-ID system in real time?

- Should Global ID matching be performed for every frame, every track, or only when a new local track is created?

- How do production retail surveillance systems normally reduce **ID switches, false merges, and identity fragmentation**?

My main objective is not just high Re-ID accuracy on a benchmark dataset. I need **stable Global IDs across multiple real-world cameras in real time**.

If anyone has worked on **multi-camera person tracking, MTMCT, Person Re-ID, retail analytics, or CCTV analytics** , I would really appreciate suggestions, papers, architectures, repositories, or practical implementation advice.

Thank you!

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**Author:** ![system](https://canada1.discourse-cdn.com/flex029/uploads/roboflow1/original/1X/bb6edbfa56d0cf2ee603f061e9e09103502cc8cc.png) [@system](https://discuss.roboflow.com/u/system)\
**Post date:** [September 9, 2026, 7:09pm UTC](https://discuss.roboflow.com/t/if-anyone-has-worked-on-multi-camera-person-tracking-mtmct-person-re-id-retail-analytics-or-cctv-analytics-i-would-really-appreciate-suggestions-papers-architectures-repositories-or-practical-implementation-advice/12435/2 "2026-09-09T19:09:14Z")

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