# Object detection and classification 

Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/camera-ai-detection-overlay-composer-display.html](https://docs.qualcomm.com/doc/80-70015-50/topic/camera-ai-detection-overlay-composer-display.html)

The **gst-camera-two-stream-detection-and-classification-side-by-side.py** script
        uses a YOLOv8 TFLite model to detect and classify objects in the scene displayed by the AI
        overlay composer.

Table : Models used for detection and classification

| Purpose | TFLite model | Description |
| :--- | :--- | :--- |
| Object detection | YOLOv8 | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ul_cfw_r4k_bdc"><br>                                <li class="li">Identify the object in a scene from a camera stream.</li><br><br>                                <li class="li">Overlay the bounding boxes over the detected objects.</li><br><br>                            </ol> |
| Image classification | Resnet101 | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_jll_v4k_bdc"><br>                                <li class="li">Classify a scene from a camera stream.</li><br><br>                                <li class="li">Overlay the classification labels on the screen.</li><br><br>                            </ol> |

## Use cases

1. Ensure that you complete the [Prerequisites](https://docs.qualcomm.com/doc/80-70015-50/topic/python-sample-applications.html#python-sample-applications__section_gm5_s5j_bdc).
2. Run the detection and classification
                    script:

        python3 /usr/bin/gst-camera-two-stream-detection-and-classification-side-by-side.pyCopy to clipboard

Table : Default directories for model and label files

| Model and label files | Directory |
| :--- | :--- |
| Detection model | /opt/data/YoloV8N\_Detection\_Quantized.tflite |
| Detection labels | /opt/data/yolov8n.labels |
| Classification model | /opt/data/Resnet101\_Quantized.tflite |
| Classification labels | /opt/data/resnet101.labels |

## Expected output

The images are shown side by side on the display.

![](data:image/png;base64,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)

## Pipeline flow

Figure : Pipeline for object detection and classification
                
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| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70015-50/topic/qtiqmmfsrc.html) | Collects two video streams from the camera:<ul class="ul"><br>                                    <li class="li">Stream for detection is split using tee and sent to the<br>                                            following:<ul class="ul" id="camera-ai-detection-overlay-composer-display__ul_elf_dtk_bdc"><br>                                            <li class="li">qtimetamux to retain the video stream.</li><br><br>                                            <li class="li">qtimlvconverter to convert the video stream to input<br>                                                tensors for the detection inference.</li><br><br>                                        </ul><br></li><br><br>                                    <li class="li">Stream for classification is split using tee and sent to the<br>                                            following:<ul class="ul" id="camera-ai-detection-overlay-composer-display__ul_nsj_htk_bdc"><br>                                            <li class="li">qtimetamux to retain the video stream.</li><br><br>                                            <li class="li">qtimlvconverter to convert the video stream to input<br>                                                tensors for the classification inference.</li><br><br>                                        </ul><br><br>                                    </li><br><br>                                </ul> |
| **Preprocessing** | **Preprocessing** |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvconverter.html) | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_i5w_4wl_vbc"><br>                                    <li class="li">Receives the video stream on its sink pad.</li><br><br>                                    <li class="li">Performs preprocessing:<ul class="ul" id="camera-ai-detection-overlay-composer-display__ol_zdw_qwl_vbc"><br>                                            <li class="li">Color conversion</li><br><br>                                            <li class="li">Scaling down/up</li><br><br>                                            <li class="li">Normalization on the stream data when the model<br>                                                expects the floating point values as an input</li><br><br>                                        </ul><br></li><br><br>                                    <li class="li">Converts the video stream to a tensor stream on its source<br>                                            pad.<p class="p">The classification model uses this tensor stream<br>                                            for inferencing.</p><br></li><br><br>                                </ol> |
| **Inferencing** | **Inferencing** |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimltflite.html) | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_u1l_cxl_vbc"><br>                                    <li class="li">Loads the model.</li><br><br>                                    <li class="li">Modifies the graph for the chosen delegate.</li><br><br>                                    <li class="li">Receives the tensor stream on its sinkpad.</li><br><br>                                    <li class="li">Runs the inference and produces a tensor stream with the<br>                                        inference results on its source pad.</li><br><br>                                </ol> |
| **Postprocessing** | **Postprocessing** |
| [qtimlvdetection](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvdetection.html) | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_ky5_grn_vbc"><br>                                    <li class="li"> Receives the inference tensors from the object detection<br>                                        model. </li><br><br>                                    <li class="li">Converts the inference tensors on its sinkpad into formats<br>                                        such as video or text that the multimedia plugins can<br>                                        process later.</li><br><br>                                    <li class="li">Applies the threshold to the chosen number of results. </li><br><br>                                    <li class="li">Loads the corresponding modules for detection models. <p class="p">In<br>                                            this use case, qtimlvdetection does the following:<br>                                            </p><ol class="ol" type="a" id="camera-ai-detection-overlay-composer-display__ol_jcd_wnk_5bc"><br>                                            <li class="li">Loads the YOLOv8 submodule. </li><br><br>                                            <li class="li">Produces results as structures of text.</li><br><br>                                            <li class="li">Sends them to the sinkpad of qtimetamux.</li><br><br>                                        </ol><br></li><br><br>                                </ol> |
| [qtimlvclassification](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvclassification.html) | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_o3v_2xl_vbc"><br>                                    <li class="li">Receives the inference results from a classification model<br>                                        on its sinkpad. </li><br><br>                                    <li class="li">Converts the inference tensors into formats such as video or<br>                                        text that the multimedia plugins can process later. </li><br><br>                                    <li class="li">Applies the threshold to the chosen number of results. </li><br><br>                                    <li class="li">Loads the corresponding modules for the classification<br>                                        models. <p class="p">In this use case, qtimlvclassification does the<br>                                            following: </p><ol class="ol" type="a" id="camera-ai-detection-overlay-composer-display__ol_p3v_2xl_vbc"><br>                                            <li class="li">Loads the submodule of the model.</li><br><br>                                            <li class="li">Produces results as video frames with classification<br>                                                labels.</li><br><br>                                            <li class="li">Sends them to the sinkpad of qtivcomposer.</li><br><br>                                        </ol><br></li><br><br>                                </ol> |
| [qtimetamux](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimetamux.html) | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_ll3_x5l_vbc"><br>                                    <li class="li">Receives video stream and text stream with bounding box<br>                                        results corresponding to the video stream on its<br>                                        sinkpads.</li><br><br>                                    <li class="li">Produces GST buffers with contents of the video stream from<br>                                        its sink pad.</li><br><br>                                    <li class="li">Adds bounding boxes as GstVideoRegionOfInterest from data<br>                                        sinkpad to GST buffers meta (meta muxing) on its source<br>                                        pad.</li><br><br>                                </ol> |
| [qtioverlay](https://docs.qualcomm.com/doc/80-70015-50/topic/qtioverlay.html) | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_wst_y5l_vbc"><br>                                    <li class="li">Receives the multiplexed stream.</li><br><br>                                    <li class="li">Overlays the bounding boxes on the VideoFrame using CL.</li><br><br>                                    <li class="li">Produces GST buffers with overlays in its source pad.</li><br><br>                                </ol> |
| [qtivcomposer](https://docs.qualcomm.com/doc/80-70015-50/topic/qtivcomposer.html) | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_nmc_lxl_vbc"><br>                                    <li class="li">Receives the original video stream with classification<br>                                        results on its sinkpads. </li><br><br>                                    <li class="li">On its sourcepad, produces GST buffers with contents<br>                                        composed of video streams from its sinkpads.</li><br><br>                                </ol> |
| **Output** | **Output** |
| [Waylandsink](https://docs.qualcomm.com/doc/80-70015-50/topic/waylandsink.html) | <ol class="ol" id="camera-ai-detection-overlay-composer-display__ol_cgt_mwl_vbc"><br>                                    <li class="li">Receives the video in its sinkpad</li><br><br>                                    <li class="li">Submits the video stream to Weston. </li><br><br>                                    <li class="li">Weston renders the video stream on a local display<br>                                        device.</li><br><br>                                </ol> |

**Parent Topic:** [Python sample applications](https://docs.qualcomm.com/doc/80-70015-50/topic/python-sample-applications.html)

Last Published: Oct 27, 2025

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