# Object detection and encode Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/camera-detection-encode.html](https://docs.qualcomm.com/doc/80-70015-50/topic/camera-detection-encode.html) The **gst-camera-detection-encode.py** uses a YOLOv8 TFLite model to identify the object in a scene from a camera stream and overlay the bounding boxes over the detected object, and then save the output to a file. ## 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 object detection and encode script: python3 /usr/bin/gst-camera-detection-encode.pyCopy to clipboard The following are the default file paths in the Python script: MODEL_FILE = "/opt/data/YOLOv8-Detection-Quantized.tflite"Copy to clipboard LABELS_FILE = "/opt/data/yolov8.labels"Copy to clipboard ## Expected output The output is saved to a file at /opt/data/det\_recording\_720p.mp4. ## Pipeline flow Figure : Pipeline for object detection and encode ![](data:image/png;base64,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) | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70015-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


  3. Converts the video stream to a tensor stream on its source
    pad.

    The object detection model uses this tensor
    stream for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimltflite](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimltflite.html) |

  1. Loads the object detection model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Runs the inference and produces a tensor stream with the
    object detection results on its source pad.


| | **Postprocessing** | **Postprocessing** | | [qtimlvdetection](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvdetection.html) |

  1. Receives the inference tensors from the object detection
    model.


  2. Converts the inference tensors on its sinkpad into formats
    such as video or text that the multimedia plugins can
    process later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules for detection models.

    In
    this use case, qtimlvdetection does the following:


    1. Loads the YOLOv8 submodule.


    2. Produces results as structures of text.


    3. Sends these results to the sinkpad of
      qtimetamux.





| | [qtimetamux](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimetamux.html) |

  1. Receives video stream and text stream with bounding box
    results corresponding to the video stream on its
    sinkpads.


  2. Produces GST buffers with contents of the video stream from
    its sink pad.


  3. Adds bounding boxes as GstVideoRegionOfInterest from data
    sinkpad to GST buffers meta (meta muxing) on its source
    pad.


| | [qtioverlay](https://docs.qualcomm.com/doc/80-70015-50/topic/qtioverlay.html) |

  1. Receives the multiplexed stream.


  2. Overlays the bounding boxes on the VideoFrame using CL.


  3. Produces GST buffers with overlays in its source pad.


| | [v4l2h264enc](https://docs.qualcomm.com/doc/80-70015-50/topic/v4l2h264enc.html) |

  1. Applies parameters to each frame of the video stream it is
    receiving on its sinkpad.


  2. Encodes it into bitstream and sends it over its
    sourcepad.


| | h264parse | Adds additional information corresponding to the bitstream to
GStreamer buffer meta. | | mp4mux | Receives these buffers and creates containers with format
specification buffers. | | **Output** | **Output** | | Filesink | Stores the resulting stream in a
/opt/data/det\_recording\_720p.mp4
file. | | Playback | Pull det\_recording\_720p.mp4 from the host
machine and play it on a media player:
`scp root@ address of target device>:/opt/data directory>` | **Parent Topic:** [Python sample applications](https://docs.qualcomm.com/doc/80-70015-50/topic/python-sample-applications.html) Last Published: Oct 27, 2025 [Previous Topic Object detection and display](https://docs.qualcomm.com/bundle/publicresource/80-70015-50/topics/camera-detection-display.md) [Next Topic Decode and object detection](https://docs.qualcomm.com/bundle/publicresource/80-70015-50/topics/decode-detection-display.md)