docs: plan v1.2 trajectory recognition workflow

docs/project.md

@@ -6,6 +6,8 @@
 
 The `v1.1 优化改造` batch upgrades the product from a fixed 8-zone layout to a configurable 1-10 zone workflow with numeric region labels and editable trash ROI calibration. All v1.1 items are part of one batch; backend, API, frontend, and documentation are implementation workstreams inside that same batch.
 
+The `v1.2 轨迹识别` batch adds source-zone trajectory evidence for disposal confirmation. The first implementation uses lightweight motion tracking and keeps YOLO disabled, while preserving an evidence contract that a later trained YOLO product detector can enrich.
+
 ## Architecture
 
 - Backend package: `src/cold_display_guard/`
@@ -15,6 +17,7 @@ The `v1.1 优化改造` batch upgrades the product from a fixed 8-zone layout to
   - `manage_api.py`: standard-library HTTP management API.
   - `main.py`: RTSP runtime loop connecting frame capture, vision, state engine, and JSONL sinks.
   - `vision.py`: heuristic ROI occupancy and trash-motion detection.
+  - v1.2 adds trajectory evidence between vision and engine: `MotionTrajectoryBackend` emits source-zone-to-trash evidence; `BatchEngine` consumes the backend-neutral `disposal_evidence` contract.
 - Frontend package: `web/`
   - Vite + vanilla JavaScript management console.
   - Default web port `23000`.
@@ -45,6 +48,13 @@ The `v1.1 优化改造` batch upgrades the product from a fixed 8-zone layout to
 - Trash ROI:
   - Stored under `[trash] roi`.
   - Does not use a food zone number.
+- v1.2 trajectory settings:
+  - `trajectory_enabled`: enables source-zone trajectory evidence.
+  - `trajectory_window_seconds`: seconds after a zone clears where movement can confirm disposal.
+  - `trajectory_sample_interval_seconds`: faster runtime delay while a candidate is active.
+  - `trajectory_min_confidence`: minimum confidence before evidence can close pending disposal.
+  - `trajectory_backend`: first valid value is `"motion"`.
+  - `yolo_enabled`: defaults to `false`; reserved for a future trained model backend.
 
 ## Event Model
 
@@ -59,6 +69,8 @@ The `v1.1 优化改造` batch upgrades the product from a fixed 8-zone layout to
 
 Events should include `zone_id`, `zone_index`, `zone_label`, `started_at`, `dwell_seconds`, and relevant alarm/removal/deadline timestamps when available.
 
+In v1.2, `batch_discarded` can be triggered by zone-scoped `disposal_evidence` before falling back to generic `trash_deposit_count`. Evidence must match the pending batch's `source_zone_id`.
+
 ## Runbook
 
 - Python tests:
@@ -79,6 +91,7 @@ Events should include `zone_id`, `zone_index`, `zone_label`, `started_at`, `dwel
 ## Known Risks
 
 - The current vision detector is heuristic and reports binary occupancy, not item counts.
+- v1.2 motion tracking improves disposal matching but can still miss movement if the hand/object path is occluded or sampled too sparsely.
 - If food is already present during baseline collection, those regions may be treated as empty baseline until visual changes occur.
 - Changing calibration while the runtime process has active batches can create operational ambiguity; v1.1 should document or enforce a pause/restart expectation.
 - Historical events must keep the zone index at the time of emission so later region reordering does not reinterpret old logs.

docs/superpowers/plans/2026-05-29-v1.2-trajectory-recognition.md

@@ -0,0 +1,169 @@
+# v1.2 Trajectory Recognition Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Add source-zone trajectory evidence so alarmed items moved to the trash are discarded by their actual source zone, while keeping YOLO as a future optional backend.
+
+**Architecture:** Extend `Observation` with backend-neutral `disposal_evidence`, make `BatchEngine` consume matching evidence before generic trash fallback, then add a no-dependency motion trajectory tracker in the vision layer. Runtime writes diagnostics and uses faster sampling only while trajectory candidates are active.
+
+**Tech Stack:** Python 3.11+ standard library, existing `Frame` RGB bytes, `unittest`, Vite/Node tests only if frontend files change.
+
+---
+
+### Task 1: Data Contract And Engine Evidence Handling
+
+**Files:**
+- Modify: `src/cold_display_guard/models.py`
+- Modify: `src/cold_display_guard/engine.py`
+- Test: `tests/test_engine.py`
+
+- [ ] **Step 1: Write failing tests**
+
+Add tests for:
+- `Observation.from_dict()` normalizes `disposal_evidence`.
+- Matching evidence discards the pending batch for the same source zone.
+- Evidence for zone 4 does not discard pending zone 1.
+- Same-observation removal plus evidence closes the newly pending batch.
+- Low-confidence evidence is ignored.
+
+- [ ] **Step 2: Run RED tests**
+
+Run: `PYTHONPATH=src python3 -m unittest tests.test_engine -v`
+
+Expected: FAIL because `Observation` has no `disposal_evidence` and engine ignores evidence.
+
+- [ ] **Step 3: Implement minimal contract and engine logic**
+
+Add a `DisposalEvidence` dataclass and `Observation.disposal_evidence`. In `BatchEngine.process()`, apply evidence to matching `pending_disposal` before generic trash deposits and again after zone transitions for same-frame removals.
+
+- [ ] **Step 4: Run GREEN tests**
+
+Run: `PYTHONPATH=src python3 -m unittest tests.test_engine -v`
+
+Expected: PASS.
+
+- [ ] **Step 5: Commit phase**
+
+Run:
+
+```bash
+git add src/cold_display_guard/models.py src/cold_display_guard/engine.py tests/test_engine.py
+git commit -m "feat: add disposal evidence engine handling"
+```
+
+### Task 2: Lightweight Motion Trajectory Backend
+
+**Files:**
+- Modify: `src/cold_display_guard/vision.py`
+- Test: `tests/test_vision.py`
+
+- [ ] **Step 1: Write failing tests**
+
+Add synthetic RGB-frame tests for:
+- Motion from source zone to trash ROI emits evidence.
+- Motion that starts away from source zone is rejected.
+- Motion that never reaches trash ROI is rejected.
+- One-frame reflection flash is rejected.
+- Multiple active candidates do not cross-close each other.
+
+- [ ] **Step 2: Run RED tests**
+
+Run: `PYTHONPATH=src python3 -m unittest tests.test_vision -v`
+
+Expected: FAIL because no trajectory tracker exists.
+
+- [ ] **Step 3: Implement minimal motion tracker**
+
+Add trajectory settings, candidate state, motion blob extraction from frame deltas, confidence scoring, and diagnostics. Keep the implementation standard-library only.
+
+- [ ] **Step 4: Run GREEN tests**
+
+Run: `PYTHONPATH=src python3 -m unittest tests.test_vision -v`
+
+Expected: PASS.
+
+- [ ] **Step 5: Commit phase**
+
+Run:
+
+```bash
+git add src/cold_display_guard/vision.py tests/test_vision.py
+git commit -m "feat: add lightweight trajectory tracking"
+```
+
+### Task 3: Runtime Configuration And Diagnostics Integration
+
+**Files:**
+- Modify: `src/cold_display_guard/main.py`
+- Modify: `src/cold_display_guard/vision.py`
+- Modify: `config/example.toml`
+- Test: `tests/test_vision.py`
+- Test: `tests/test_main.py`
+
+- [ ] **Step 1: Write failing tests**
+
+Add tests that verify runtime defaults include trajectory settings with YOLO disabled and diagnostics rows include emitted evidence when present.
+
+- [ ] **Step 2: Run RED tests**
+
+Run: `PYTHONPATH=src python3 -m unittest tests.test_vision tests.test_main -v`
+
+Expected: FAIL because runtime does not pass evidence into `Observation` or expose trajectory sampling state.
+
+- [ ] **Step 3: Implement runtime integration**
+
+Return `disposal_evidence` from vision observation, write it to diagnostics, pass it to `Observation`, and use `trajectory_sample_interval_seconds` while candidates are active.
+
+- [ ] **Step 4: Run GREEN tests**
+
+Run: `PYTHONPATH=src python3 -m unittest tests.test_vision tests.test_main -v`
+
+Expected: PASS.
+
+- [ ] **Step 5: Commit phase**
+
+Run:
+
+```bash
+git add src/cold_display_guard/main.py src/cold_display_guard/vision.py config/example.toml tests/test_vision.py tests/test_main.py
+git commit -m "feat: integrate trajectory runtime diagnostics"
+```
+
+### Task 4: Documentation, Full Verification, And Deployment Prep
+
+**Files:**
+- Modify: `README_zh.md`
+- Modify: `docs/project.md`
+- Modify: `task_plan.md`
+- Modify: `findings.md`
+- Modify: `progress.md`
+- Modify: `memories.md`
+
+- [ ] **Step 1: Update docs**
+
+Document v1.2 trajectory settings, evidence semantics, tests, and remote deployment notes.
+
+- [ ] **Step 2: Run full verification**
+
+Run:
+
+```bash
+PYTHONPATH=src python3 -m unittest discover -s tests -v
+node --test web/test/zone-state.test.js
+cd web && pnpm build
+```
+
+Expected: PASS for all commands. If frontend files did not change, frontend commands still provide regression coverage for the management console.
+
+- [ ] **Step 3: Commit phase**
+
+Run:
+
+```bash
+git add README_zh.md docs/project.md task_plan.md findings.md progress.md memories.md docs/superpowers/plans/2026-05-29-v1.2-trajectory-recognition.md
+git commit -m "docs: document v1.2 trajectory recognition"
+```
+
+- [ ] **Step 4: Prepare remote deploy**
+
+Use rsync excluding `config/example.toml`, rebuild runtime/API, and verify Docker services. Record the exact commands and results in `progress.md`.

findings.md

@@ -52,6 +52,31 @@
 
 Use `阶段 x` only as a workflow stage inside the same `v1.1 优化改造` batch.
 
+## v1.2 轨迹识别 Findings
+
+## Architecture
+
+- `main.py` 当前每轮从 RTSP 截一帧，调用 `ZoneOccupancyDetector.observe()` 得到 `zone_counts`、`trash_deposit_count`、`diagnostics`，再构造 `Observation` 给 `BatchEngine.process()`。
+- `vision.py` 当前只有区域占用和垃圾桶动作计数，没有从“来源区域到垃圾桶”的轨迹证据。
+- `engine.py` 当前先处理 pending 过期和旧 trash deposit，再处理区域转移，最后对同帧剩余 trash deposit 做兜底；这为同帧 evidence 处理提供了插入点。
+- `models.py` 的 `Observation` 是最适合扩展统一 evidence contract 的位置。
+- v1.2 设计规格已保存并提交：`docs/superpowers/specs/2026-05-29-lightweight-trajectory-yolo-ready-design.md`，commit `ac6d368`。
+
+## Constraints
+
+- 第一版必须在无 YOLO 依赖下运行。
+- 轨迹检测只应在区域变空后的短窗口活跃，避免持续 CPU 压力。
+- 垃圾桶内部不可见；确认点是进入垃圾桶口 ROI，不是看到桶内物品。
+- 不能让一个来源区域的 evidence 关闭另一个区域的 pending batch。
+- 远端部署必须继续排除 `config/example.toml`，以保留 RTSP 和现场标定。
+
+## Decisions
+
+- 新字段命名为 `disposal_evidence`，元素使用 `source_zone_id`、`target`、`confidence`、`method`、`track_points`、可选 `item_class`、`detector_score`。
+- `trash_deposit_count` 保留兼容和兜底，但 engine 先使用 zone-scoped evidence。
+- 轨迹诊断必须记录 active、emitted、rejected、expired，方便现场调参。
+- 后续 YOLO 模型只作为 evidence 增强输入，不能修改 `BatchEngine` 的业务语义。
+
 ## Backend Planning Notes
 
 - `EngineSettings.zone_ids` should remain config driven; numeric zones are preferred for new configs, but old `r1c1` style IDs should continue loading.

memories.md

@@ -0,0 +1,24 @@
+# Memories
+
+## User and Workflow
+
+- User wants Chinese responses.
+- Project path for subagent headers is `/Users/yoilun/Code/cold_display_guard`.
+- Current workflow batch is `v1.2 轨迹识别`.
+- User requested following `/Users/yoilun/Code/goal-subagents-workflow-prompt.md`.
+- Every subagent task must begin with:
+
+```text
+[项目: /Users/yoilun/Code/cold_display_guard]
+[工作流批次: v1.2 轨迹识别]
+[阶段: 阶段 x]
+[角色: 对应智能体角色]
+```
+
+## Technical Direction
+
+- First implement lightweight motion trajectory detection without YOLO dependencies.
+- Preserve a stable `disposal_evidence` contract for a future trained YOLO product detector.
+- Keep ROI occupancy timing as the source of zone occupied/empty state.
+- Use trajectory evidence before generic trash-motion FIFO fallback.
+- Remote deployment target is `xiaozheng@192.168.5.206:/home/xiaozheng/cold_display_guard`; preserve remote `config/example.toml`.

progress.md

@@ -183,3 +183,23 @@
 | Runtime restart can drop threshold-edge timers | Runtime restore used raw threshold recompute instead of stable `occupied`, so a restart during a one-frame raw dip could lose an active timer | Restore now uses stable `occupied` when present and keeps raw recompute only as a fallback | Resolved; regression test covers zone 2-style flicker |
 | Zone 1 timer reset | Zone 1's ROI had too few samples with the default stride `8`; dark evidence jumped between `0.0714` and `0.0357/0`, causing two occupied frames followed by two empty frames and repeated short batches | Reduced default and remote `sample_stride_pixels` to `4` so small ROI/object evidence is less quantized | Resolved in current verification; zone 1 remains continuously occupied after deploy |
 | Zone 1/4 disposal missed after zone 2 discard | Zone 2 generated a trash deposit at `14:32:13`; zone 1/4 cleared at `14:32:19`, but their strong trash motion was inside the old 8-second cooldown, and one same-frame deposit could only discard one newly pending batch | Reduced trash cooldown to 3 seconds and let one same-frame trash motion discard all newly pending alerted batches from that observation | Resolved for future events; historical zone 1/4 rows are not rewritten |
+
+## 2026-05-29 v1.2 轨迹识别
+
+### Session Log
+
+| Time | Phase | Actor | Action | Result |
+| --- | --- | --- | --- | --- |
+| 2026-05-29 | Setup | Main Agent | Created Goal for `v1.2 轨迹识别` using `/Users/yoilun/Code/cold_display_guard` as the real project path | Active goal tracks lightweight trajectory detection plus YOLO-ready evidence contract |
+| 2026-05-29 | Setup | Main Agent | Read `/Users/yoilun/Code/goal-subagents-workflow-prompt.md` | Workflow requires task files, stage-based coding/testing agents, bug loop limit, and standard subagent context header |
+| 2026-05-29 | Setup | Main Agent | Read existing `task_plan.md`, `findings.md`, `progress.md`, and `docs/project.md` | v1.1 state is complete; v1.2 plan can start on branch `lightweight-trajectory-tracking` |
+
+### Test Results
+
+| Time | Command | Result | Notes |
+| --- | --- | --- | --- |
+
+### Bug Loop
+
+| Phase | Bug | Fix Attempt | Retest Result |
+| --- | --- | --- | --- |

task_plan.md

@@ -76,3 +76,42 @@ Create and evolve an independent git project under `~/Code` for monitoring food
 ```
 
 其中 `阶段 x` 表示同一 `v1.1 优化改造` 批次内的工作阶段，不代表拆分成独立批次。
+
+## v1.2 轨迹识别
+
+### Goal
+
+在 `/Users/yoilun/Code/cold_display_guard` 中完成轨迹识别改造：保留现有 ROI 占用计时和垃圾桶动作兜底，新增轻量轨迹移动检测，输出可被未来 YOLO 物品识别模型复用的统一 `disposal_evidence`，让报警后移出的物品按来源区域确认是否进入垃圾桶。
+
+### Stop Conditions
+
+- [ ] v1.2 所有阶段完成。
+- [ ] 必要 Python 测试通过。
+- [ ] 前端测试或构建在受影响时通过。
+- [ ] `docs/project.md` 记录 v1.2 架构、配置、运行方式和关键决策。
+- [ ] 没有 blocking bug 或未处理的高风险问题。
+- [ ] 如果同一问题连续 3 次修复仍失败，暂停并报告原因、已尝试方案和建议下一步。
+
+### Phases
+
+| Phase | Status | Goal | Acceptance Criteria |
+| --- | --- | --- | --- |
+| 1 | pending | 建立 `disposal_evidence` 数据契约并让状态机优先按来源区域丢弃 | `Observation` 支持 evidence；engine 能按 `source_zone_id` 精确关闭 pending batch；同帧移除+evidence 有回归测试；旧 `trash_deposit_count` 仍可兜底 |
+| 2 | pending | 实现无 YOLO 依赖的轻量轨迹检测 | synthetic frame 测试覆盖源区域到垃圾桶、非源区域运动、未到垃圾桶、单帧反光、多候选互不串扰；不引入模型依赖 |
+| 3 | pending | 集成 runtime 配置、诊断和候选窗口加速采样 | `main.py` 写入 `disposal_evidence` 与 trajectory diagnostics；配置默认 `trajectory_enabled=true`、`yolo_enabled=false`；候选活跃时使用更短采样间隔 |
+| 4 | pending | 文档、全量验证和部署准备 | README/project/progress 更新；Python 全量测试通过；前端测试/构建按影响范围验证；远端部署命令和风险记录清楚 |
+
+### v1.2 Decisions
+
+- 第一版使用 `MotionTrajectoryBackend`，不安装 YOLO、PyTorch、ONNX Runtime 或 OpenVINO。
+- YOLO 作为后续 `YoloDetectionBackend` 接入统一 evidence contract，不能绕过轨迹校验直接关闭业务事件。
+- 状态机只消费 `disposal_evidence`，不依赖具体视觉后端。
+- 轨迹 evidence 优先级高于 FIFO 垃圾桶动作兜底。
+- 子 agent 派发必须使用标准上下文头：
+
+```text
+[项目: /Users/yoilun/Code/cold_display_guard]
+[工作流批次: v1.2 轨迹识别]
+[阶段: 阶段 x]
+[角色: 对应智能体角色]
+```