| --- |
| license: mit |
| license_link: LICENSE |
| library_name: openvino |
| pipeline_tag: image-classification |
| tags: |
| - openvino |
| - intel |
| - person-detection |
| - person-attributes |
| - appearance-search |
| - attribute-recognition |
| - edge-ai |
| - metro |
| - dlstreamer |
| language: |
| - en |
| --- |
| |
| # Appearance-Based Search |
|
|
| | Property | Value | |
| |---|---| |
| | **Category** | Person Detection + Appearance Attribute Search | |
| | **Base Model** | [person-detection-retail-0013](https://docs.openvino.ai/2024/omz_models_model_person_detection_retail_0013.html) + [person-attributes-recognition-crossroad-0230](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/person-attributes-recognition-crossroad-0230/README.md) (Open Model Zoo) | |
| | **Source Framework** | Caffe / PyTorch (Open Model Zoo) | |
| | **Supported Precisions** | FP32, FP16 | |
| | **Inference Engine** | OpenVINO | |
| | **Hardware** | CPU, GPU, NPU | |
| | **Detected Class(es)** | Persons (detection) + 8 appearance attributes (gender, bag, backpack, hat, sleeve length, trouser length, hair length, coat/jacket) | |
|
|
| --- |
|
|
| ## Overview |
|
|
| Appearance-Based Search is a Metro Analytics use case that finds every person |
| in a video whose visible appearance matches a described set of attributes. |
| Instead of comparing against a reference photo, the operator specifies *what a |
| person looks like* -- for example "a person with long hair" -- and the pipeline |
| highlights only the people who match that description. |
|
|
| It uses a two-stage pipeline: |
|
|
| - **person-detection-retail-0013** -- detects every person in the scene. |
| - **person-attributes-recognition-crossroad-0230** -- classifies each detected |
| person with eight binary appearance attributes: |
| `is_male`, `has_bag`, `has_backpack`, `has_hat`, `has_longsleeves`, |
| `has_longpants`, `has_longhair`, and `has_coat_jacket`. |
|
|
| Each detected person is scored on all eight attributes, thresholded, and |
| compared to the search query. Only people who satisfy every requested attribute |
| are boxed, so the annotated output shows the search result directly. |
|
|
| The search query is expressed as a small dictionary. Set an attribute to `True` |
| to require it or `False` to require its absence, and omit the attributes you do |
| not care about: |
|
|
| ```python |
| QUERY = {"has_longhair": True} # anyone with long hair |
| QUERY = {"is_male": True, "has_hat": True} # men wearing a hat |
| QUERY = {"has_backpack": True} # anyone with a backpack |
| ``` |
|
|
| Typical Metro deployments include: |
|
|
| - **Suspect / Person-of-Interest Search** -- scan recorded footage for people |
| matching a witness description ("man with a backpack and a hat"). |
| - **Lost Property** -- locate the person who was carrying a particular bag. |
| - **Retail and Transit Analytics** -- count shoppers or passengers with |
| specific appearance traits over time. |
| - **Operational Triage** -- narrow a large camera archive to a short list of |
| candidate clips before manual review. |
|
|
| > **Privacy Note:** Appearance attribute recognition processes |
| > biometric-adjacent data. |
| > Ensure your deployment complies with applicable privacy regulations |
| > (GDPR, BIPA, etc.) and has proper consent and retention policies in place. |
|
|
| --- |
|
|
| ## Prerequisites |
|
|
| - Python 3.11+ |
| - [Install OpenVINO](https://docs.openvino.ai/2026/get-started/install-openvino.html) (latest version) |
| - [Install Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/get_started/install/install_guide_ubuntu.html) (latest version) |
|
|
| Create and activate a Python virtual environment before running the scripts: |
|
|
| ```bash |
| python3 -m venv .venv --system-site-packages |
| source .venv/bin/activate |
| ``` |
|
|
| > **Note:** The `--system-site-packages` flag is required so the virtual |
| > environment can access the system-installed OpenVINO and DLStreamer Python |
| > packages. |
|
|
| --- |
|
|
| ## Getting Started |
|
|
| ### Download Models |
|
|
| Run the provided script to download the person detection and person attributes |
| models from the Open Model Zoo: |
|
|
| ```bash |
| chmod +x export_and_quantize.sh |
| ./export_and_quantize.sh |
| ``` |
|
|
| The script downloads `person-detection-retail-0013` and |
| `person-attributes-recognition-crossroad-0230` in FP16 and downloads the sample |
| retail-aisle video (`test_video.mp4`) that the samples search. |
|
|
| ### OpenVINO Sample |
|
|
| The sample below searches the video for people whose appearance matches the |
| `QUERY`. In each sampled frame it detects every person, classifies the eight |
| appearance attributes for each one, and keeps only the people who satisfy the |
| query. It saves the frame containing the most matching people, drawing a green |
| box on every matched person. |
| Change the `device` string to run on CPU, GPU, or NPU. |
|
|
| ```python |
| import cv2 |
| import numpy as np |
| import openvino as ov |
| |
| DETECTION_MODEL = "intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml" |
| ATTRIBUTES_MODEL = "intel/person-attributes-recognition-crossroad-0230/FP16/person-attributes-recognition-crossroad-0230.xml" |
| SCENE_VIDEO = "test_video.mp4" |
| |
| # person-attributes-recognition-crossroad-0230 emits eight binary appearance |
| # attributes (output layer "453"), in this order: |
| ATTRIBUTE_NAMES = [ |
| "is_male", "has_bag", "has_backpack", "has_hat", |
| "has_longsleeves", "has_longpants", "has_longhair", "has_coat_jacket", |
| ] |
| |
| # The appearance being searched for. Set an attribute to True to require it or |
| # False to require its absence; omit attributes you do not care about. |
| QUERY = {"has_longhair": True} |
| |
| CONF_THRESHOLD = 0.6 # person-detection confidence |
| ATTR_THRESHOLD = 0.5 # attribute presence threshold |
| |
| core = ov.Core() |
| |
| # Change device to "GPU" or "NPU" to run on integrated GPU or NPU. |
| det_compiled = core.compile_model(core.read_model(DETECTION_MODEL), "CPU") |
| attr_compiled = core.compile_model(core.read_model(ATTRIBUTES_MODEL), "CPU") |
| |
| det_input = det_compiled.input(0) |
| det_h, det_w = det_input.shape[2], det_input.shape[3] |
| attr_input = attr_compiled.input(0) |
| attr_h, attr_w = attr_input.shape[2], attr_input.shape[3] |
| attr_output = attr_compiled.output("453") |
| |
| |
| def detect_persons(img): |
| h0, w0 = img.shape[:2] |
| blob = cv2.resize(img, (det_w, det_h)) |
| blob = blob.transpose(2, 0, 1)[np.newaxis, ...].astype(np.float32) |
| dets = det_compiled([blob])[det_compiled.output(0)][0][0] |
| persons = [] |
| for d in dets: |
| if float(d[2]) < CONF_THRESHOLD: |
| continue |
| x1 = max(0, int(d[3] * w0)) |
| y1 = max(0, int(d[4] * h0)) |
| x2 = min(w0, int(d[5] * w0)) |
| y2 = min(h0, int(d[6] * h0)) |
| if x2 > x1 and y2 > y1: |
| persons.append((x1, y1, x2, y2)) |
| return persons |
| |
| |
| def get_attributes(img, bbox): |
| x1, y1, x2, y2 = bbox |
| crop = img[y1:y2, x1:x2] |
| blob = cv2.resize(crop, (attr_w, attr_h)) |
| blob = blob.transpose(2, 0, 1)[np.newaxis, ...].astype(np.float32) |
| values = attr_compiled([blob])[attr_output].flatten() |
| return {name: float(values[i]) for i, name in enumerate(ATTRIBUTE_NAMES)} |
| |
| |
| def matches_query(attributes): |
| return all((attributes[name] >= ATTR_THRESHOLD) == wanted |
| for name, wanted in QUERY.items()) |
| |
| |
| # Scan the scene and keep the frame containing the most people whose appearance |
| # matches the query, so the saved image best illustrates the search result. |
| cap = cv2.VideoCapture(SCENE_VIDEO) |
| total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) or 900 |
| best = {"count": 0, "frame": None, "boxes": []} |
| for frame_idx in range(0, total, 15): |
| cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx) |
| ok, frame = cap.read() |
| if not ok: |
| break |
| boxes = [bbox for bbox in detect_persons(frame) |
| if matches_query(get_attributes(frame, bbox))] |
| if len(boxes) > best["count"]: |
| best = {"count": len(boxes), "frame": frame.copy(), "boxes": boxes} |
| cap.release() |
| |
| if best["frame"] is None or best["count"] == 0: |
| raise SystemExit("No person matching the appearance query was found") |
| |
| # Draw a green box on every person that matches the searched appearance. |
| query_text = ", ".join(k if v else f"no {k}" for k, v in QUERY.items()) |
| frame = best["frame"] |
| for x1, y1, x2, y2 in best["boxes"]: |
| cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) |
| cv2.putText(frame, f"MATCH: {query_text}", (x1, max(15, y1 - 8)), |
| cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) |
| print(f"Found {best['count']} person(s) matching [{query_text}]") |
| |
| cv2.imwrite("output_openvino.jpg", frame) |
| print("Saved: output_openvino.jpg") |
| ``` |
|
|
| **Device targets:** |
|
|
| - `"CPU"` -- default, works on all Intel platforms. |
| - `"GPU"` -- Intel integrated or discrete GPU. |
| - `"NPU"` -- Intel NPU; both FP16 models are NPU-compatible. |
|
|
| #### Expected Output |
|
|
|  |
|
|
| ### DLStreamer Sample |
|
|
| The pipeline below runs the person detector via `gvadetect` and the appearance |
| attribute classifier via `gvaclassify` on the video. |
| Frames are pulled through an `appsink`, where each detected person's eight |
| appearance attributes are read from the classification tensor, thresholded, and |
| compared to the `QUERY`. |
| Only people whose appearance matches the query are boxed, so the annotated |
| `output_dlstreamer.mp4` highlights exactly the people the search is looking for. |
|
|
| > **Notes on running this sample:** |
| > |
| > - Export `PYTHONPATH` so the DLStreamer Python modules (`gi`, `gstgva`) are |
| > importable: |
| > |
| > ```bash |
| > source /opt/intel/openvino_2026/setupvars.sh |
| > source /opt/intel/dlstreamer/scripts/setup_dls_env.sh |
| > export PYTHONPATH=/opt/intel/dlstreamer/python:\ |
| > /opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-} |
| > ``` |
| > |
| > - The attribute scores are attached as a tensor on each person's |
| > region-of-interest metadata. Convert the stream to `BGR` **before** |
| > `gvadetect`/`gvaclassify` so a downstream format conversion does not strip |
| > those tensors before the `appsink` reads them. |
| |
| ```python |
| import gi |
| |
| gi.require_version("Gst", "1.0") |
| from gi.repository import Gst |
|
|
| Gst.init([]) |
|
|
| import numpy as np |
| import cv2 |
| from gstgva import VideoFrame |
|
|
| INPUT_VIDEO = "test_video.mp4" |
| OUTPUT_VIDEO = "output_dlstreamer.mp4" |
| DETECTION_MODEL = "intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml" |
| ATTRIBUTES_MODEL = "intel/person-attributes-recognition-crossroad-0230/FP16/person-attributes-recognition-crossroad-0230.xml" |
| # For CPU: change "GPU" to "CPU". For NPU: change "GPU" to "NPU". |
| DEVICE = "GPU" |
| DET_THRESHOLD = 0.6 |
| ATTR_THRESHOLD = 0.5 |
|
|
| # person-attributes-recognition-crossroad-0230 emits eight binary appearance |
| # attributes (output layer "453"), in this order: |
| ATTRIBUTE_NAMES = [ |
| "is_male", "has_bag", "has_backpack", "has_hat", |
| "has_longsleeves", "has_longpants", "has_longhair", "has_coat_jacket", |
| ] |
|
|
| # The appearance being searched for. Set an attribute to True to require it or |
| # False to require its absence; omit attributes you do not care about. |
| QUERY = {"has_longhair": True} |
| |
| |
| def person_attributes(video_frame): |
| """Yield ((x, y, w, h), {attribute: score}) for each classified person.""" |
| for region in video_frame.regions(): |
| rect = region.rect() |
| scores = None |
| for tensor in region.tensors(): |
| if tensor.is_detection(): |
| continue |
| data = np.array(tensor.data(), dtype=np.float32) |
| # The attributes vector is the length-8 output ("453"); the model |
| # also emits two length-2 colour points that are ignored here. |
| if data.size == len(ATTRIBUTE_NAMES): |
| scores = {name: float(data[i]) |
| for i, name in enumerate(ATTRIBUTE_NAMES)} |
| if scores is None: |
| continue |
| yield (int(rect.x), int(rect.y), int(rect.w), int(rect.h)), scores |
| |
|
|
| def matches_query(scores): |
| return all((scores[name] >= ATTR_THRESHOLD) == wanted |
| for name, wanted in QUERY.items()) |
| |
|
|
| query_text = ", ".join(k if v else f"no {k}" for k, v in QUERY.items()) |
| writer = {"w": None} |
| match_frames = 0 |
|
|
|
|
| def on_video(sink): |
| global match_frames |
| sample = sink.emit("pull-sample") |
| if sample is None: |
| return Gst.FlowReturn.OK |
| vf = VideoFrame(sample.get_buffer(), caps=sample.get_caps()) |
| matches = [(x, y, w, h) for (x, y, w, h), scores in person_attributes(vf) |
| if matches_query(scores)] |
| |
| with vf.data() as mat: |
| frame = mat.copy() |
| |
| if writer["w"] is None: |
| frame_h, frame_w = frame.shape[:2] |
| structure = sample.get_caps().get_structure(0) |
| ok_fr, fps_n, fps_d = structure.get_fraction("framerate") |
| fps = fps_n / fps_d if ok_fr and fps_d else 12 |
| writer["w"] = cv2.VideoWriter( |
| OUTPUT_VIDEO, cv2.VideoWriter_fourcc(*"mp4v"), fps, (frame_w, frame_h)) |
| |
| for x, y, w, h in matches: |
| cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) |
| cv2.putText(frame, f"MATCH: {query_text}", (x, max(15, y - 8)), |
| cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) |
| if matches: |
| match_frames += 1 |
| writer["w"].write(frame) |
| return Gst.FlowReturn.OK |
| |
|
|
| # Convert to BGR before inference so gvaclassify's attribute tensors survive to |
| # the appsink (a later format-changing videoconvert would strip them). |
| pipeline = Gst.parse_launch( |
| f"filesrc location={INPUT_VIDEO} ! decodebin3 ! videoconvert ! " |
| f"video/x-raw,format=BGR ! " |
| f"gvadetect model={DETECTION_MODEL} device={DEVICE} " |
| f"threshold={DET_THRESHOLD} ! queue ! " |
| f"gvaclassify model={ATTRIBUTES_MODEL} device={DEVICE} ! queue ! " |
| "appsink name=sink emit-signals=true sync=false max-buffers=4 drop=false" |
| ) |
| sink = pipeline.get_by_name("sink") |
| sink.connect("new-sample", on_video) |
| pipeline.set_state(Gst.State.PLAYING) |
| pipeline.get_bus().timed_pop_filtered( |
| Gst.CLOCK_TIME_NONE, Gst.MessageType.EOS | Gst.MessageType.ERROR) |
| pipeline.set_state(Gst.State.NULL) |
| if writer["w"] is not None: |
| writer["w"].release() |
| print(f"Frames with a person matching [{query_text}]: {match_frames}", flush=True) |
| print(f"Saved: {OUTPUT_VIDEO}", flush=True) |
| ``` |
| |
| **Device targets:** |
|
|
| - `DEVICE = "GPU"` -- default in the sample code. |
| - `DEVICE = "CPU"` -- change `"GPU"` to `"CPU"`. |
| - `DEVICE = "NPU"` -- change `"GPU"` to `"NPU"`; use `batch-size=1` and `nireq=4` for best NPU utilization. |
|
|
| #### Expected Output |
|
|
|  |
|
|
| --- |
|
|
| ## License |
|
|
| Licensed under the MIT License. See [LICENSE](LICENSE) for details. |
|
|
| ## References |
|
|
| - [person-detection-retail-0013](https://docs.openvino.ai/2024/omz_models_model_person_detection_retail_0013.html) |
| - [person-attributes-recognition-crossroad-0230](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/person-attributes-recognition-crossroad-0230/README.md) |
| - [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) |
| - [OpenVINO Documentation](https://docs.openvino.ai/) |
| - [Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/index.html) |
|
|
